CNN model.fit 모델 학습 (브라켓)

※ 텐서플로우 2.0 변환

In [0]:
# 런타임 -> 런타임 유형변경 -> 하드웨어 가속도 TPU변경
%tensorflow_version 2.x
#런타임 -> 런타임 다시시작

※ 데이터 다운로드

아래의 다운로드 링크를 눌러주세요

data_3000 다운로드 링크

data_1000 다운로드 링크

1. DATA Load

In [2]:
from google.colab import files 

uploaded = files.upload()

for fn in uploaded.keys():
  print('User uploaded file "{name}" with length {length} bytes'.format(
      name=fn, length=len(uploaded[fn]))) 
Upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable.
Saving data_3000.zip to data_3000.zip
User uploaded file "data_3000.zip" with length 3309996 bytes

드라이브 마운트에 연결하기

In [0]:
from google.colab import drive
drive.mount('/content/drive')
Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly

Enter your authorization code:
··········
Mounted at /content/drive
In [0]:
! mkdir data_3000                       # 마운트에 폴더 생성
! unzip data_3000.zip -d ./data_3000    # unzip 

2. 파일 내 이미지 불러오기

In [4]:
import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
import os 

# 압축해제된 데이터 경로를 찾아 복사해서 붙여넣어주세요 (마지막 '/' 꼭 붙여야함)
src = './data_3000/'

# 이미지 읽기 및 출력
def img_read_plot(src,file):
    img = cv.imread(src+file,cv.COLOR_BGR2GRAY)
    plt.imshow(img)
    plt.xticks([]) # x축 눈금
    plt.yticks([]) # y축 눈금
    plt.show()
    return img

# 이미지 읽기
def img_read(src,file):
    img = cv.imread(src+file,cv.COLOR_BGR2GRAY)
    return img

# src 경로에 있는 파일 명을 저장합니다. 
files = os.listdir(src)

X,Y = [],[]
count = 0

# 경로와 파일명을 입력으로 넣어 확인하고 
# 데이터를 255로 나눠서 0~1사이로 정규화 하여 X 리스트에 넣습니다. 
for file in files: 
  # 데이터의 일부분만 확인해봅니다.
  if count < 10 : 
    print(count)
    X.append(img_read_plot(src,file)/255.)
    Y.append(float(file[:-4]))
    count+=1
  else : 
    X.append(img_read(src,file)/255.)
    Y.append(float(file[:-4]))

# array로 데이터 변환
X = np.array(X)
Y = np.array(Y)

print('X_shape:',np.shape(X[0]),'Y_shape:',np.shape(Y[0]))
print('X_list shape:',np.shape(X),'Y_list shape:',np.shape(Y))
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X_shape: (56, 56) Y_shape: ()
X_list shape: (3000, 56, 56) Y_list shape: (3000,)

3. Importing Libraries

In [5]:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
from tensorflow import keras
from sklearn.model_selection import train_test_split

print(tf.__version__)     # 텐서플로우 버전확인 (colab의 기본버전은 1.15.0) --> 2.0 변경 "%tensorflow_version 2.x"
print(keras.__version__)
2.2.0-rc4
2.3.0-tf

4. Hyper Parameters

In [0]:
learning_rate = 0.001  # 러닝레이트 
training_epochs = 1000  # 에폭
batch_size = 32       # 배치사이즈

5. Data Split

In [7]:
import sklearn
from sklearn.model_selection import train_test_split

# Train set(80%), Test set(20%)으로 나누기 
train_images, test_images, train_labels, test_labels = train_test_split(X,Y, test_size=0.2, random_state=1,shuffle=True)

# CNN layer에 들어갈 수 있게 (x, 56, 56, 1) 차원으로 맞춰줌 
train_images = np.expand_dims(train_images, axis=-1)
test_images = np.expand_dims(test_images, axis=-1)

print(np.shape(train_images),np.shape(test_images))
print(np.shape(train_labels),np.shape(test_labels))
(2400, 56, 56, 1) (600, 56, 56, 1)
(2400,) (600,)

6. Model Function

In [0]:
# Sequential 모델 층 구성하기
def create_model():
    model = keras.Sequential() 
    model.add(keras.layers.Conv2D(filters=32, kernel_size=3, activation=tf.nn.relu, padding='SAME', 
                                  input_shape=(56, 56, 1)))
    model.add(keras.layers.MaxPool2D(padding='SAME'))
    model.add(keras.layers.Conv2D(filters=64, kernel_size=3, activation=tf.nn.relu, padding='SAME'))
    model.add(keras.layers.MaxPool2D(padding='SAME'))
    model.add(keras.layers.Conv2D(filters=128, kernel_size=3, activation=tf.nn.relu, padding='SAME'))
    model.add(keras.layers.MaxPool2D(padding='SAME'))
    model.add(keras.layers.Flatten())
    model.add(keras.layers.Dense(256, activation=tf.nn.relu))
    model.add(keras.layers.Dropout(0.5))
    model.add(keras.layers.Dense(1))
    return model
In [9]:
model = create_model() # 모델 함수를 model로 변경
model.summary() # 모델에 대한 요약 출력해줌
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 56, 56, 32)        320       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 28, 28, 32)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 28, 28, 64)        18496     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 14, 14, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 14, 14, 128)       73856     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 7, 7, 128)         0         
_________________________________________________________________
flatten (Flatten)            (None, 6272)              0         
_________________________________________________________________
dense (Dense)                (None, 256)               1605888   
_________________________________________________________________
dropout (Dropout)            (None, 256)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 257       
=================================================================
Total params: 1,698,817
Trainable params: 1,698,817
Non-trainable params: 0
_________________________________________________________________
In [10]:
# 위에서 정한 모델을 그림으로(plot) 보여줌
keras.utils.plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=True) 
Out[10]:

7. Data training

In [11]:
import keras.backend as K

def rmse (y_true, y_pred):  # RMSE : root mean square error
  return K.sqrt(K.mean(K.square(y_pred -y_true), axis=-1))

# CNN 모델 구조 확정하고 컴파일 진행
model.compile(loss='MSE',                   # MSE : mean square error
              optimizer='adam',                 
              metrics=[ rmse, 'mape' ])     # MAPE : mean absolute percentage error     

# 학습실행
history = model.fit(train_images, train_labels,       # 입력값
          batch_size=batch_size,                      # 1회마다 배치마다 100개 프로세스 
          epochs=training_epochs,                     # 1000회 학습
          verbose=1,                                  # verbose는 학습 중 출력되는 문구를 설정하는 것 
          validation_data=(test_images, test_labels)) # test를 val로 사용

# test 값 결과 확인
score = model.evaluate(test_images, test_labels, verbose=0) # verbose가 0 이면 ==== 움직이지 않고, 1이면 ==== 진행 바가 움직임
print('Test loss(mse) :', score[0])
print('Test RMSE :', score[1])
print('Test MAPE :', score[2])
Using TensorFlow backend.
Epoch 1/1000
75/75 [==============================] - 1s 8ms/step - loss: 54.6676 - rmse: 5.0051 - mape: 18.4058 - val_loss: 5.2949 - val_rmse: 1.9162 - val_mape: 6.8353
Epoch 2/1000
75/75 [==============================] - 0s 5ms/step - loss: 12.1542 - rmse: 2.7933 - mape: 10.2840 - val_loss: 2.0514 - val_rmse: 1.1837 - val_mape: 4.4427
Epoch 3/1000
75/75 [==============================] - 0s 5ms/step - loss: 12.0381 - rmse: 2.7886 - mape: 10.2634 - val_loss: 1.6969 - val_rmse: 1.0863 - val_mape: 3.9716
Epoch 4/1000
75/75 [==============================] - 0s 5ms/step - loss: 11.3765 - rmse: 2.7053 - mape: 9.9441 - val_loss: 1.5748 - val_rmse: 1.0261 - val_mape: 3.7180
Epoch 5/1000
75/75 [==============================] - 0s 5ms/step - loss: 12.2342 - rmse: 2.7971 - mape: 10.2901 - val_loss: 8.8666 - val_rmse: 2.7836 - val_mape: 10.0837
Epoch 6/1000
75/75 [==============================] - 0s 5ms/step - loss: 10.5738 - rmse: 2.5591 - mape: 9.4188 - val_loss: 3.5365 - val_rmse: 1.6628 - val_mape: 5.9917
Epoch 7/1000
75/75 [==============================] - 0s 5ms/step - loss: 10.3935 - rmse: 2.5855 - mape: 9.5060 - val_loss: 0.6943 - val_rmse: 0.6491 - val_mape: 2.3601
Epoch 8/1000
75/75 [==============================] - 0s 5ms/step - loss: 10.8929 - rmse: 2.6375 - mape: 9.6974 - val_loss: 1.0354 - val_rmse: 0.7927 - val_mape: 2.8363
Epoch 9/1000
75/75 [==============================] - 0s 5ms/step - loss: 10.8147 - rmse: 2.6312 - mape: 9.6705 - val_loss: 2.8456 - val_rmse: 1.5254 - val_mape: 5.7079
Epoch 10/1000
75/75 [==============================] - 0s 5ms/step - loss: 10.4849 - rmse: 2.6097 - mape: 9.6000 - val_loss: 1.2440 - val_rmse: 0.9573 - val_mape: 3.5642
Epoch 11/1000
75/75 [==============================] - 0s 5ms/step - loss: 9.8470 - rmse: 2.5102 - mape: 9.2336 - val_loss: 0.8664 - val_rmse: 0.7548 - val_mape: 2.7274
Epoch 12/1000
75/75 [==============================] - 0s 5ms/step - loss: 9.7662 - rmse: 2.4786 - mape: 9.1176 - val_loss: 0.5549 - val_rmse: 0.6117 - val_mape: 2.2666
Epoch 13/1000
75/75 [==============================] - 0s 5ms/step - loss: 9.6682 - rmse: 2.4755 - mape: 9.1188 - val_loss: 0.7827 - val_rmse: 0.7032 - val_mape: 2.5367
Epoch 14/1000
75/75 [==============================] - 0s 5ms/step - loss: 10.1626 - rmse: 2.5551 - mape: 9.3921 - val_loss: 1.9459 - val_rmse: 1.2637 - val_mape: 4.7189
Epoch 15/1000
75/75 [==============================] - 0s 5ms/step - loss: 10.1060 - rmse: 2.5433 - mape: 9.3539 - val_loss: 0.6319 - val_rmse: 0.6055 - val_mape: 2.1715
Epoch 16/1000
75/75 [==============================] - 0s 5ms/step - loss: 9.9885 - rmse: 2.5275 - mape: 9.3067 - val_loss: 1.2112 - val_rmse: 0.9275 - val_mape: 3.3506
Epoch 17/1000
75/75 [==============================] - 0s 5ms/step - loss: 10.8773 - rmse: 2.6427 - mape: 9.7088 - val_loss: 0.8173 - val_rmse: 0.7731 - val_mape: 2.8754
Epoch 18/1000
75/75 [==============================] - 0s 5ms/step - loss: 9.0232 - rmse: 2.3700 - mape: 8.7249 - val_loss: 0.3638 - val_rmse: 0.4705 - val_mape: 1.7079
Epoch 19/1000
75/75 [==============================] - 0s 5ms/step - loss: 9.3166 - rmse: 2.4500 - mape: 9.0114 - val_loss: 2.2162 - val_rmse: 1.3731 - val_mape: 5.1056
Epoch 20/1000
75/75 [==============================] - 0s 5ms/step - loss: 10.7463 - rmse: 2.6093 - mape: 9.5997 - val_loss: 0.5343 - val_rmse: 0.5611 - val_mape: 2.0142
Epoch 21/1000
75/75 [==============================] - 0s 5ms/step - loss: 9.9073 - rmse: 2.5221 - mape: 9.2785 - val_loss: 0.3325 - val_rmse: 0.4618 - val_mape: 1.6911
Epoch 22/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.9471 - rmse: 2.4090 - mape: 8.8599 - val_loss: 1.0194 - val_rmse: 0.8601 - val_mape: 3.1011
Epoch 23/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.9798 - rmse: 2.3844 - mape: 8.7735 - val_loss: 0.3734 - val_rmse: 0.4711 - val_mape: 1.7017
Epoch 24/1000
75/75 [==============================] - 0s 5ms/step - loss: 9.1852 - rmse: 2.4158 - mape: 8.8788 - val_loss: 0.6277 - val_rmse: 0.6111 - val_mape: 2.1890
Epoch 25/1000
75/75 [==============================] - 0s 5ms/step - loss: 9.1402 - rmse: 2.4006 - mape: 8.8326 - val_loss: 0.3281 - val_rmse: 0.4573 - val_mape: 1.6732
Epoch 26/1000
75/75 [==============================] - 0s 5ms/step - loss: 10.0118 - rmse: 2.5274 - mape: 9.3009 - val_loss: 0.8359 - val_rmse: 0.7911 - val_mape: 2.9592
Epoch 27/1000
75/75 [==============================] - 0s 5ms/step - loss: 9.3323 - rmse: 2.4149 - mape: 8.8809 - val_loss: 3.0316 - val_rmse: 1.6508 - val_mape: 6.0200
Epoch 28/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.6984 - rmse: 2.3557 - mape: 8.6548 - val_loss: 0.5306 - val_rmse: 0.5738 - val_mape: 2.0772
Epoch 29/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.7053 - rmse: 2.3574 - mape: 8.6715 - val_loss: 0.2982 - val_rmse: 0.4298 - val_mape: 1.5721
Epoch 30/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.6321 - rmse: 2.3543 - mape: 8.6641 - val_loss: 1.5575 - val_rmse: 1.1445 - val_mape: 4.2460
Epoch 31/1000
75/75 [==============================] - 0s 5ms/step - loss: 9.8901 - rmse: 2.4985 - mape: 9.2002 - val_loss: 1.0024 - val_rmse: 0.8481 - val_mape: 3.0486
Epoch 32/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.8409 - rmse: 2.3624 - mape: 8.6867 - val_loss: 0.3664 - val_rmse: 0.4937 - val_mape: 1.8184
Epoch 33/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.6593 - rmse: 2.3454 - mape: 8.6342 - val_loss: 0.3242 - val_rmse: 0.4536 - val_mape: 1.6678
Epoch 34/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.5608 - rmse: 2.3543 - mape: 8.6582 - val_loss: 0.3831 - val_rmse: 0.4766 - val_mape: 1.7178
Epoch 35/1000
75/75 [==============================] - 0s 5ms/step - loss: 9.1568 - rmse: 2.4301 - mape: 8.9421 - val_loss: 0.4951 - val_rmse: 0.5729 - val_mape: 2.0891
Epoch 36/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.7962 - rmse: 2.3792 - mape: 8.7561 - val_loss: 0.3845 - val_rmse: 0.5049 - val_mape: 1.8642
Epoch 37/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.8491 - rmse: 2.3878 - mape: 8.7866 - val_loss: 0.4077 - val_rmse: 0.5293 - val_mape: 1.9658
Epoch 38/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.4514 - rmse: 2.3243 - mape: 8.5555 - val_loss: 0.4077 - val_rmse: 0.4858 - val_mape: 1.7512
Epoch 39/1000
75/75 [==============================] - 0s 5ms/step - loss: 10.1775 - rmse: 2.5441 - mape: 9.3465 - val_loss: 0.5946 - val_rmse: 0.6016 - val_mape: 2.1612
Epoch 40/1000
75/75 [==============================] - 0s 5ms/step - loss: 9.3106 - rmse: 2.4383 - mape: 8.9705 - val_loss: 1.7526 - val_rmse: 1.1941 - val_mape: 4.3151
Epoch 41/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.6788 - rmse: 2.3653 - mape: 8.7062 - val_loss: 0.2754 - val_rmse: 0.4163 - val_mape: 1.5208
Epoch 42/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.8549 - rmse: 2.3692 - mape: 8.6979 - val_loss: 0.3211 - val_rmse: 0.4419 - val_mape: 1.5984
Epoch 43/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.3512 - rmse: 2.3066 - mape: 8.4835 - val_loss: 0.7721 - val_rmse: 0.7417 - val_mape: 2.6734
Epoch 44/1000
75/75 [==============================] - 0s 5ms/step - loss: 9.1097 - rmse: 2.3766 - mape: 8.7474 - val_loss: 0.2983 - val_rmse: 0.4402 - val_mape: 1.6097
Epoch 45/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.4711 - rmse: 2.3191 - mape: 8.5418 - val_loss: 0.5863 - val_rmse: 0.6133 - val_mape: 2.2063
Epoch 46/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.5622 - rmse: 2.3366 - mape: 8.6002 - val_loss: 1.1348 - val_rmse: 0.9535 - val_mape: 3.4686
Epoch 47/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.1526 - rmse: 2.2771 - mape: 8.3892 - val_loss: 0.4773 - val_rmse: 0.5533 - val_mape: 2.0075
Epoch 48/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.9668 - rmse: 2.3968 - mape: 8.8175 - val_loss: 0.2712 - val_rmse: 0.4182 - val_mape: 1.5240
Epoch 49/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.4096 - rmse: 2.3030 - mape: 8.4622 - val_loss: 1.6080 - val_rmse: 1.1642 - val_mape: 4.3376
Epoch 50/1000
75/75 [==============================] - 0s 5ms/step - loss: 9.2810 - rmse: 2.4238 - mape: 8.9085 - val_loss: 0.7543 - val_rmse: 0.7485 - val_mape: 2.7943
Epoch 51/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.9078 - rmse: 2.3910 - mape: 8.7975 - val_loss: 1.2289 - val_rmse: 0.9671 - val_mape: 3.4965
Epoch 52/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.8231 - rmse: 2.3662 - mape: 8.6982 - val_loss: 0.2967 - val_rmse: 0.4385 - val_mape: 1.6008
Epoch 53/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.1407 - rmse: 2.2682 - mape: 8.3412 - val_loss: 0.6895 - val_rmse: 0.6839 - val_mape: 2.4675
Epoch 54/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.3989 - rmse: 2.2933 - mape: 8.4466 - val_loss: 0.3077 - val_rmse: 0.4414 - val_mape: 1.6144
Epoch 55/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.4352 - rmse: 2.3143 - mape: 8.5130 - val_loss: 1.2680 - val_rmse: 1.0122 - val_mape: 3.6834
Epoch 56/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.5215 - rmse: 2.3326 - mape: 8.5840 - val_loss: 1.3207 - val_rmse: 1.0231 - val_mape: 3.6997
Epoch 57/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.1608 - rmse: 2.2628 - mape: 8.3211 - val_loss: 3.3266 - val_rmse: 1.7544 - val_mape: 6.4061
Epoch 58/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.5038 - rmse: 2.3359 - mape: 8.5978 - val_loss: 0.6473 - val_rmse: 0.6785 - val_mape: 2.4900
Epoch 59/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.0429 - rmse: 2.2592 - mape: 8.3043 - val_loss: 0.5576 - val_rmse: 0.6130 - val_mape: 2.2098
Epoch 60/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.0034 - rmse: 2.2630 - mape: 8.3316 - val_loss: 1.0697 - val_rmse: 0.9170 - val_mape: 3.3263
Epoch 61/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.3422 - rmse: 2.2935 - mape: 8.4429 - val_loss: 0.2989 - val_rmse: 0.4243 - val_mape: 1.5391
Epoch 62/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.5286 - rmse: 2.3411 - mape: 8.6203 - val_loss: 0.5749 - val_rmse: 0.6201 - val_mape: 2.2361
Epoch 63/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.0900 - rmse: 2.2693 - mape: 8.3482 - val_loss: 0.7604 - val_rmse: 0.7273 - val_mape: 2.6289
Epoch 64/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.3708 - rmse: 2.3116 - mape: 8.4871 - val_loss: 0.3059 - val_rmse: 0.4487 - val_mape: 1.6629
Epoch 65/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.2547 - rmse: 2.3065 - mape: 8.4824 - val_loss: 0.4059 - val_rmse: 0.5028 - val_mape: 1.8158
Epoch 66/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.6496 - rmse: 2.3445 - mape: 8.6237 - val_loss: 0.8937 - val_rmse: 0.8011 - val_mape: 2.8810
Epoch 67/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.8654 - rmse: 2.2324 - mape: 8.2094 - val_loss: 0.4568 - val_rmse: 0.5704 - val_mape: 2.1224
Epoch 68/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.0919 - rmse: 2.2618 - mape: 8.3165 - val_loss: 0.3196 - val_rmse: 0.4483 - val_mape: 1.6190
Epoch 69/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.4666 - rmse: 2.3268 - mape: 8.5436 - val_loss: 0.3958 - val_rmse: 0.5112 - val_mape: 1.8509
Epoch 70/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.9546 - rmse: 2.2590 - mape: 8.3054 - val_loss: 0.2232 - val_rmse: 0.3639 - val_mape: 1.3258
Epoch 71/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.6537 - rmse: 2.2367 - mape: 8.2339 - val_loss: 0.2882 - val_rmse: 0.4242 - val_mape: 1.5571
Epoch 72/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.5334 - rmse: 2.3323 - mape: 8.5675 - val_loss: 0.7462 - val_rmse: 0.7390 - val_mape: 2.6791
Epoch 73/1000
75/75 [==============================] - 0s 6ms/step - loss: 8.1706 - rmse: 2.2913 - mape: 8.4182 - val_loss: 0.4042 - val_rmse: 0.5140 - val_mape: 1.8645
Epoch 74/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.8017 - rmse: 2.3646 - mape: 8.6968 - val_loss: 0.7436 - val_rmse: 0.7111 - val_mape: 2.5549
Epoch 75/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.8995 - rmse: 2.2431 - mape: 8.2395 - val_loss: 0.3873 - val_rmse: 0.4887 - val_mape: 1.7561
Epoch 76/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.0910 - rmse: 2.2605 - mape: 8.3122 - val_loss: 0.3905 - val_rmse: 0.4884 - val_mape: 1.7599
Epoch 77/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.1245 - rmse: 2.2757 - mape: 8.3761 - val_loss: 1.1822 - val_rmse: 0.9908 - val_mape: 3.6797
Epoch 78/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.0784 - rmse: 2.2523 - mape: 8.2812 - val_loss: 0.4402 - val_rmse: 0.5347 - val_mape: 1.9262
Epoch 79/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.1022 - rmse: 2.2832 - mape: 8.3940 - val_loss: 0.3726 - val_rmse: 0.5066 - val_mape: 1.8860
Epoch 80/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.9971 - rmse: 2.2525 - mape: 8.2803 - val_loss: 1.2225 - val_rmse: 0.9609 - val_mape: 3.4639
Epoch 81/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.4754 - rmse: 2.1889 - mape: 8.0416 - val_loss: 1.1112 - val_rmse: 0.9484 - val_mape: 3.4753
Epoch 82/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.2574 - rmse: 2.3013 - mape: 8.4625 - val_loss: 0.3599 - val_rmse: 0.4764 - val_mape: 1.7492
Epoch 83/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.9519 - rmse: 2.2595 - mape: 8.3144 - val_loss: 0.4308 - val_rmse: 0.5211 - val_mape: 1.8776
Epoch 84/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.1132 - rmse: 2.2649 - mape: 8.3314 - val_loss: 0.6332 - val_rmse: 0.6584 - val_mape: 2.3832
Epoch 85/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.5514 - rmse: 2.1987 - mape: 8.0779 - val_loss: 1.2382 - val_rmse: 1.0027 - val_mape: 3.6330
Epoch 86/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.1068 - rmse: 2.2348 - mape: 8.2146 - val_loss: 0.5227 - val_rmse: 0.5932 - val_mape: 2.1474
Epoch 87/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.1950 - rmse: 2.2680 - mape: 8.3320 - val_loss: 0.4049 - val_rmse: 0.5001 - val_mape: 1.7977
Epoch 88/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.9829 - rmse: 2.2411 - mape: 8.2536 - val_loss: 0.7159 - val_rmse: 0.7266 - val_mape: 2.6410
Epoch 89/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.7501 - rmse: 2.2145 - mape: 8.1388 - val_loss: 0.3560 - val_rmse: 0.4957 - val_mape: 1.8531
Epoch 90/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.6928 - rmse: 2.1957 - mape: 8.0863 - val_loss: 0.6482 - val_rmse: 0.6867 - val_mape: 2.5010
Epoch 91/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.5332 - rmse: 2.2114 - mape: 8.1353 - val_loss: 0.9436 - val_rmse: 0.8526 - val_mape: 3.0977
Epoch 92/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.3381 - rmse: 2.1474 - mape: 7.8997 - val_loss: 0.2322 - val_rmse: 0.3777 - val_mape: 1.3788
Epoch 93/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.4203 - rmse: 2.3185 - mape: 8.5281 - val_loss: 1.8438 - val_rmse: 1.2492 - val_mape: 4.5310
Epoch 94/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.5340 - rmse: 2.1900 - mape: 8.0629 - val_loss: 0.4958 - val_rmse: 0.5785 - val_mape: 2.0886
Epoch 95/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.5233 - rmse: 2.1790 - mape: 8.0031 - val_loss: 0.8354 - val_rmse: 0.8122 - val_mape: 3.0214
Epoch 96/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.6972 - rmse: 2.2166 - mape: 8.1550 - val_loss: 0.3883 - val_rmse: 0.5097 - val_mape: 1.8597
Epoch 97/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.6749 - rmse: 2.2195 - mape: 8.1684 - val_loss: 0.2858 - val_rmse: 0.4159 - val_mape: 1.5004
Epoch 98/1000
75/75 [==============================] - 0s 5ms/step - loss: 8.0639 - rmse: 2.2405 - mape: 8.2481 - val_loss: 0.4339 - val_rmse: 0.5499 - val_mape: 2.0632
Epoch 99/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.8755 - rmse: 2.2314 - mape: 8.2072 - val_loss: 1.0425 - val_rmse: 0.9111 - val_mape: 3.4187
Epoch 100/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.9260 - rmse: 2.2393 - mape: 8.2344 - val_loss: 0.3337 - val_rmse: 0.4583 - val_mape: 1.6614
Epoch 101/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.3174 - rmse: 2.1544 - mape: 7.9150 - val_loss: 0.2618 - val_rmse: 0.4046 - val_mape: 1.4641
Epoch 102/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.6660 - rmse: 2.2041 - mape: 8.0996 - val_loss: 0.3480 - val_rmse: 0.4591 - val_mape: 1.6603
Epoch 103/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.4632 - rmse: 2.1640 - mape: 7.9571 - val_loss: 0.5639 - val_rmse: 0.6186 - val_mape: 2.2317
Epoch 104/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.2304 - rmse: 2.1433 - mape: 7.8780 - val_loss: 0.6177 - val_rmse: 0.6633 - val_mape: 2.4193
Epoch 105/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.5602 - rmse: 2.1964 - mape: 8.0739 - val_loss: 0.7283 - val_rmse: 0.7495 - val_mape: 2.7362
Epoch 106/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.5427 - rmse: 2.2030 - mape: 8.1012 - val_loss: 1.1868 - val_rmse: 0.9882 - val_mape: 3.6030
Epoch 107/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.2683 - rmse: 2.1530 - mape: 7.9214 - val_loss: 1.2482 - val_rmse: 1.0093 - val_mape: 3.7847
Epoch 108/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.6245 - rmse: 2.2122 - mape: 8.1402 - val_loss: 0.3257 - val_rmse: 0.4454 - val_mape: 1.6209
Epoch 109/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.2960 - rmse: 2.1715 - mape: 7.9737 - val_loss: 0.7875 - val_rmse: 0.7506 - val_mape: 2.7127
Epoch 110/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.0689 - rmse: 2.1132 - mape: 7.7762 - val_loss: 0.7881 - val_rmse: 0.7586 - val_mape: 2.7394
Epoch 111/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.4838 - rmse: 2.1812 - mape: 8.0166 - val_loss: 0.4302 - val_rmse: 0.5097 - val_mape: 1.8252
Epoch 112/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.3142 - rmse: 2.1734 - mape: 7.9891 - val_loss: 0.2325 - val_rmse: 0.3627 - val_mape: 1.3125
Epoch 113/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.4054 - rmse: 2.1715 - mape: 7.9838 - val_loss: 0.2378 - val_rmse: 0.3939 - val_mape: 1.4444
Epoch 114/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.0800 - rmse: 2.1373 - mape: 7.8641 - val_loss: 0.2781 - val_rmse: 0.4133 - val_mape: 1.5143
Epoch 115/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.2302 - rmse: 2.1470 - mape: 7.8988 - val_loss: 0.2583 - val_rmse: 0.4017 - val_mape: 1.4652
Epoch 116/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.7126 - rmse: 2.1977 - mape: 8.0803 - val_loss: 0.2377 - val_rmse: 0.3686 - val_mape: 1.3406
Epoch 117/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.2036 - rmse: 2.1359 - mape: 7.8575 - val_loss: 0.2843 - val_rmse: 0.4105 - val_mape: 1.4957
Epoch 118/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.2587 - rmse: 2.1541 - mape: 7.9175 - val_loss: 1.9489 - val_rmse: 1.3055 - val_mape: 4.8561
Epoch 119/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.4831 - rmse: 2.2058 - mape: 8.1202 - val_loss: 0.9922 - val_rmse: 0.8290 - val_mape: 2.9678
Epoch 120/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.5236 - rmse: 2.1792 - mape: 8.0022 - val_loss: 0.2593 - val_rmse: 0.3902 - val_mape: 1.4353
Epoch 121/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.3443 - rmse: 2.1620 - mape: 7.9415 - val_loss: 0.2411 - val_rmse: 0.3798 - val_mape: 1.3888
Epoch 122/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.7928 - rmse: 2.2211 - mape: 8.1710 - val_loss: 0.3575 - val_rmse: 0.4907 - val_mape: 1.8222
Epoch 123/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.0429 - rmse: 2.1069 - mape: 7.7374 - val_loss: 0.3232 - val_rmse: 0.4414 - val_mape: 1.5933
Epoch 124/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.2034 - rmse: 2.1436 - mape: 7.8851 - val_loss: 0.3184 - val_rmse: 0.4467 - val_mape: 1.6295
Epoch 125/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.1493 - rmse: 2.1305 - mape: 7.8264 - val_loss: 0.2654 - val_rmse: 0.4175 - val_mape: 1.5401
Epoch 126/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.2267 - rmse: 2.1372 - mape: 7.8556 - val_loss: 1.7775 - val_rmse: 1.2550 - val_mape: 4.5763
Epoch 127/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.5186 - rmse: 2.1956 - mape: 8.0723 - val_loss: 0.2682 - val_rmse: 0.4087 - val_mape: 1.4985
Epoch 128/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.1354 - rmse: 2.1332 - mape: 7.8450 - val_loss: 0.6252 - val_rmse: 0.6770 - val_mape: 2.4648
Epoch 129/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.1059 - rmse: 2.1325 - mape: 7.8399 - val_loss: 1.2302 - val_rmse: 1.0118 - val_mape: 3.7148
Epoch 130/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.9243 - rmse: 2.0927 - mape: 7.6946 - val_loss: 0.3184 - val_rmse: 0.4603 - val_mape: 1.6938
Epoch 131/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.8666 - rmse: 2.0838 - mape: 7.6632 - val_loss: 0.2880 - val_rmse: 0.4108 - val_mape: 1.4833
Epoch 132/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.6424 - rmse: 2.0600 - mape: 7.5690 - val_loss: 0.2428 - val_rmse: 0.3824 - val_mape: 1.3897
Epoch 133/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.0974 - rmse: 2.1264 - mape: 7.8164 - val_loss: 0.3385 - val_rmse: 0.4596 - val_mape: 1.6666
Epoch 134/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.1372 - rmse: 2.1034 - mape: 7.7199 - val_loss: 0.7319 - val_rmse: 0.7489 - val_mape: 2.7979
Epoch 135/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.7926 - rmse: 2.0894 - mape: 7.6794 - val_loss: 0.4286 - val_rmse: 0.5214 - val_mape: 1.8863
Epoch 136/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.7875 - rmse: 2.0797 - mape: 7.6451 - val_loss: 0.4354 - val_rmse: 0.5247 - val_mape: 1.8836
Epoch 137/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.1057 - rmse: 2.1268 - mape: 7.8160 - val_loss: 0.7649 - val_rmse: 0.7439 - val_mape: 2.6855
Epoch 138/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.9583 - rmse: 2.1141 - mape: 7.7886 - val_loss: 0.3054 - val_rmse: 0.4291 - val_mape: 1.5561
Epoch 139/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.3469 - rmse: 2.1663 - mape: 7.9567 - val_loss: 0.3194 - val_rmse: 0.4404 - val_mape: 1.5947
Epoch 140/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.0244 - rmse: 2.0903 - mape: 7.6821 - val_loss: 0.9473 - val_rmse: 0.8490 - val_mape: 3.0715
Epoch 141/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.7649 - rmse: 2.0696 - mape: 7.6079 - val_loss: 0.2969 - val_rmse: 0.4311 - val_mape: 1.5771
Epoch 142/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.2406 - rmse: 2.1358 - mape: 7.8515 - val_loss: 0.3912 - val_rmse: 0.5255 - val_mape: 1.9601
Epoch 143/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.0367 - rmse: 2.1039 - mape: 7.7253 - val_loss: 0.6123 - val_rmse: 0.6680 - val_mape: 2.4313
Epoch 144/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.6689 - rmse: 2.0605 - mape: 7.5791 - val_loss: 0.4707 - val_rmse: 0.5638 - val_mape: 2.0394
Epoch 145/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.0481 - rmse: 2.1165 - mape: 7.7845 - val_loss: 0.2551 - val_rmse: 0.3933 - val_mape: 1.4351
Epoch 146/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.7572 - rmse: 2.0658 - mape: 7.5879 - val_loss: 0.6682 - val_rmse: 0.6989 - val_mape: 2.5357
Epoch 147/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.7484 - rmse: 2.0536 - mape: 7.5511 - val_loss: 0.2183 - val_rmse: 0.3624 - val_mape: 1.3230
Epoch 148/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.7153 - rmse: 2.0502 - mape: 7.5409 - val_loss: 0.6672 - val_rmse: 0.6968 - val_mape: 2.5682
Epoch 149/1000
75/75 [==============================] - 0s 5ms/step - loss: 7.0374 - rmse: 2.1177 - mape: 7.7956 - val_loss: 0.2465 - val_rmse: 0.3774 - val_mape: 1.3726
Epoch 150/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.7930 - rmse: 2.0831 - mape: 7.6716 - val_loss: 0.8019 - val_rmse: 0.7754 - val_mape: 2.8094
Epoch 151/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.2452 - rmse: 1.9918 - mape: 7.3231 - val_loss: 1.6430 - val_rmse: 1.1880 - val_mape: 4.3240
Epoch 152/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.3379 - rmse: 2.0013 - mape: 7.3484 - val_loss: 1.2021 - val_rmse: 0.9996 - val_mape: 3.6617
Epoch 153/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.3078 - rmse: 2.0018 - mape: 7.3576 - val_loss: 0.3794 - val_rmse: 0.4998 - val_mape: 1.8227
Epoch 154/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.4646 - rmse: 2.0297 - mape: 7.4575 - val_loss: 0.2051 - val_rmse: 0.3561 - val_mape: 1.3039
Epoch 155/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.5134 - rmse: 2.0500 - mape: 7.5445 - val_loss: 0.3474 - val_rmse: 0.4649 - val_mape: 1.6789
Epoch 156/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.3346 - rmse: 1.9967 - mape: 7.3371 - val_loss: 0.2923 - val_rmse: 0.4295 - val_mape: 1.5575
Epoch 157/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.5711 - rmse: 2.0615 - mape: 7.5780 - val_loss: 0.2778 - val_rmse: 0.4320 - val_mape: 1.6066
Epoch 158/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.1192 - rmse: 1.9599 - mape: 7.2145 - val_loss: 0.5976 - val_rmse: 0.6567 - val_mape: 2.3751
Epoch 159/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.4810 - rmse: 2.0423 - mape: 7.5068 - val_loss: 0.2147 - val_rmse: 0.3635 - val_mape: 1.3242
Epoch 160/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.0690 - rmse: 1.9538 - mape: 7.1890 - val_loss: 1.1825 - val_rmse: 0.9876 - val_mape: 3.5853
Epoch 161/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.7257 - rmse: 2.0687 - mape: 7.6062 - val_loss: 0.2767 - val_rmse: 0.4229 - val_mape: 1.5558
Epoch 162/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.4337 - rmse: 2.0296 - mape: 7.4646 - val_loss: 0.3683 - val_rmse: 0.4950 - val_mape: 1.8285
Epoch 163/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.6508 - rmse: 2.0501 - mape: 7.5387 - val_loss: 0.6519 - val_rmse: 0.6911 - val_mape: 2.5365
Epoch 164/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.2735 - rmse: 2.0009 - mape: 7.3559 - val_loss: 0.9433 - val_rmse: 0.8503 - val_mape: 3.0713
Epoch 165/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.4591 - rmse: 2.0252 - mape: 7.4498 - val_loss: 0.6615 - val_rmse: 0.6916 - val_mape: 2.5022
Epoch 166/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.3828 - rmse: 2.0176 - mape: 7.4172 - val_loss: 0.2438 - val_rmse: 0.3926 - val_mape: 1.4371
Epoch 167/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.1214 - rmse: 1.9771 - mape: 7.2710 - val_loss: 0.3492 - val_rmse: 0.4785 - val_mape: 1.7423
Epoch 168/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.2554 - rmse: 2.0073 - mape: 7.3773 - val_loss: 0.5777 - val_rmse: 0.6379 - val_mape: 2.3213
Epoch 169/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.0039 - rmse: 1.9503 - mape: 7.1624 - val_loss: 0.2721 - val_rmse: 0.4031 - val_mape: 1.4575
Epoch 170/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.4312 - rmse: 2.0133 - mape: 7.4022 - val_loss: 1.1745 - val_rmse: 0.9929 - val_mape: 3.6126
Epoch 171/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.6994 - rmse: 2.0499 - mape: 7.5411 - val_loss: 0.4576 - val_rmse: 0.5312 - val_mape: 1.8991
Epoch 172/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.3429 - rmse: 2.0034 - mape: 7.3611 - val_loss: 0.4383 - val_rmse: 0.5323 - val_mape: 1.9191
Epoch 173/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.5041 - rmse: 2.0298 - mape: 7.4670 - val_loss: 0.2137 - val_rmse: 0.3572 - val_mape: 1.2968
Epoch 174/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.3979 - rmse: 2.0139 - mape: 7.3910 - val_loss: 0.2509 - val_rmse: 0.3937 - val_mape: 1.4412
Epoch 175/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.2526 - rmse: 1.9996 - mape: 7.3460 - val_loss: 0.2731 - val_rmse: 0.4045 - val_mape: 1.4605
Epoch 176/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.0902 - rmse: 1.9873 - mape: 7.3153 - val_loss: 1.0678 - val_rmse: 0.9353 - val_mape: 3.4000
Epoch 177/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.2899 - rmse: 2.0136 - mape: 7.4069 - val_loss: 0.5146 - val_rmse: 0.6006 - val_mape: 2.1787
Epoch 178/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.3192 - rmse: 2.0091 - mape: 7.3794 - val_loss: 0.6008 - val_rmse: 0.6264 - val_mape: 2.2429
Epoch 179/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.3143 - rmse: 2.0094 - mape: 7.3977 - val_loss: 0.9685 - val_rmse: 0.8645 - val_mape: 3.1374
Epoch 180/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.8589 - rmse: 1.9325 - mape: 7.1062 - val_loss: 0.2308 - val_rmse: 0.3716 - val_mape: 1.3612
Epoch 181/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.5669 - rmse: 2.0343 - mape: 7.4734 - val_loss: 1.5255 - val_rmse: 1.1261 - val_mape: 4.0752
Epoch 182/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.2353 - rmse: 1.9968 - mape: 7.3351 - val_loss: 0.2153 - val_rmse: 0.3695 - val_mape: 1.3502
Epoch 183/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.0318 - rmse: 1.9645 - mape: 7.2187 - val_loss: 0.3232 - val_rmse: 0.4565 - val_mape: 1.6626
Epoch 184/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.0497 - rmse: 1.9852 - mape: 7.3049 - val_loss: 0.1959 - val_rmse: 0.3462 - val_mape: 1.2615
Epoch 185/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.9993 - rmse: 1.9698 - mape: 7.2260 - val_loss: 0.4384 - val_rmse: 0.5324 - val_mape: 1.9182
Epoch 186/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.0477 - rmse: 1.9621 - mape: 7.2148 - val_loss: 0.5486 - val_rmse: 0.6018 - val_mape: 2.1567
Epoch 187/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.7562 - rmse: 1.9060 - mape: 7.0030 - val_loss: 0.3202 - val_rmse: 0.4483 - val_mape: 1.6233
Epoch 188/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.9942 - rmse: 1.9505 - mape: 7.1638 - val_loss: 2.3470 - val_rmse: 1.4692 - val_mape: 5.3782
Epoch 189/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.1142 - rmse: 1.9759 - mape: 7.2699 - val_loss: 0.3777 - val_rmse: 0.5155 - val_mape: 1.9136
Epoch 190/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.1137 - rmse: 1.9659 - mape: 7.2332 - val_loss: 0.7344 - val_rmse: 0.7542 - val_mape: 2.7536
Epoch 191/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.9500 - rmse: 1.9411 - mape: 7.1370 - val_loss: 0.3676 - val_rmse: 0.4722 - val_mape: 1.7070
Epoch 192/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.7846 - rmse: 1.9237 - mape: 7.0724 - val_loss: 0.2268 - val_rmse: 0.3729 - val_mape: 1.3594
Epoch 193/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.5571 - rmse: 1.8754 - mape: 6.8917 - val_loss: 0.2759 - val_rmse: 0.4091 - val_mape: 1.5080
Epoch 194/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.7021 - rmse: 1.8965 - mape: 6.9654 - val_loss: 0.2079 - val_rmse: 0.3547 - val_mape: 1.2970
Epoch 195/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.8565 - rmse: 1.9639 - mape: 7.2197 - val_loss: 0.4256 - val_rmse: 0.5359 - val_mape: 1.9476
Epoch 196/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.1077 - rmse: 1.9727 - mape: 7.2550 - val_loss: 0.4040 - val_rmse: 0.5058 - val_mape: 1.8134
Epoch 197/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.6217 - rmse: 1.9033 - mape: 7.0041 - val_loss: 0.9013 - val_rmse: 0.8481 - val_mape: 3.0940
Epoch 198/1000
75/75 [==============================] - 0s 5ms/step - loss: 6.0963 - rmse: 1.9575 - mape: 7.1993 - val_loss: 0.2209 - val_rmse: 0.3527 - val_mape: 1.2783
Epoch 199/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.8517 - rmse: 1.9285 - mape: 7.0845 - val_loss: 1.3202 - val_rmse: 1.0325 - val_mape: 3.7405
Epoch 200/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.9758 - rmse: 1.9611 - mape: 7.2105 - val_loss: 0.3953 - val_rmse: 0.5072 - val_mape: 1.8374
Epoch 201/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.7269 - rmse: 1.9049 - mape: 7.0105 - val_loss: 1.0158 - val_rmse: 0.9011 - val_mape: 3.2758
Epoch 202/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.4915 - rmse: 1.8587 - mape: 6.8296 - val_loss: 0.3365 - val_rmse: 0.4767 - val_mape: 1.7583
Epoch 203/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.7736 - rmse: 1.9029 - mape: 6.9909 - val_loss: 0.2006 - val_rmse: 0.3501 - val_mape: 1.2870
Epoch 204/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.6843 - rmse: 1.8938 - mape: 6.9630 - val_loss: 0.2493 - val_rmse: 0.4028 - val_mape: 1.4693
Epoch 205/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.6536 - rmse: 1.8852 - mape: 6.9392 - val_loss: 1.4285 - val_rmse: 1.1164 - val_mape: 4.0770
Epoch 206/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.8765 - rmse: 1.9345 - mape: 7.1134 - val_loss: 0.7446 - val_rmse: 0.7580 - val_mape: 2.7507
Epoch 207/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.7007 - rmse: 1.9019 - mape: 6.9932 - val_loss: 1.2585 - val_rmse: 1.0287 - val_mape: 3.8283
Epoch 208/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.7262 - rmse: 1.9160 - mape: 7.0406 - val_loss: 0.3467 - val_rmse: 0.4649 - val_mape: 1.6890
Epoch 209/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.5383 - rmse: 1.8609 - mape: 6.8344 - val_loss: 0.5324 - val_rmse: 0.6148 - val_mape: 2.2471
Epoch 210/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.8372 - rmse: 1.9317 - mape: 7.1059 - val_loss: 0.3114 - val_rmse: 0.4415 - val_mape: 1.5923
Epoch 211/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.8868 - rmse: 1.9456 - mape: 7.1559 - val_loss: 0.3404 - val_rmse: 0.4609 - val_mape: 1.6608
Epoch 212/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.3218 - rmse: 1.8518 - mape: 6.8132 - val_loss: 0.2007 - val_rmse: 0.3485 - val_mape: 1.2683
Epoch 213/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.5344 - rmse: 1.8682 - mape: 6.8622 - val_loss: 0.8228 - val_rmse: 0.8080 - val_mape: 2.9439
Epoch 214/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.6033 - rmse: 1.8841 - mape: 6.9280 - val_loss: 0.3183 - val_rmse: 0.4521 - val_mape: 1.6522
Epoch 215/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.3926 - rmse: 1.8612 - mape: 6.8443 - val_loss: 1.0777 - val_rmse: 0.9392 - val_mape: 3.4221
Epoch 216/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.6606 - rmse: 1.9164 - mape: 7.0426 - val_loss: 0.7067 - val_rmse: 0.7342 - val_mape: 2.7219
Epoch 217/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.3558 - rmse: 1.8457 - mape: 6.7779 - val_loss: 0.5572 - val_rmse: 0.6376 - val_mape: 2.3323
Epoch 218/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.6419 - rmse: 1.8843 - mape: 6.9157 - val_loss: 0.9394 - val_rmse: 0.8298 - val_mape: 2.9930
Epoch 219/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.7859 - rmse: 1.9360 - mape: 7.1156 - val_loss: 0.6798 - val_rmse: 0.7201 - val_mape: 2.6138
Epoch 220/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.4128 - rmse: 1.8639 - mape: 6.8522 - val_loss: 0.2050 - val_rmse: 0.3522 - val_mape: 1.2788
Epoch 221/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.3640 - rmse: 1.8369 - mape: 6.7483 - val_loss: 0.2014 - val_rmse: 0.3477 - val_mape: 1.2722
Epoch 222/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.4771 - rmse: 1.8367 - mape: 6.7482 - val_loss: 0.1978 - val_rmse: 0.3455 - val_mape: 1.2555
Epoch 223/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.4135 - rmse: 1.8552 - mape: 6.8177 - val_loss: 0.6046 - val_rmse: 0.6589 - val_mape: 2.3925
Epoch 224/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.4960 - rmse: 1.8520 - mape: 6.8148 - val_loss: 0.2222 - val_rmse: 0.3634 - val_mape: 1.3216
Epoch 225/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.4403 - rmse: 1.8405 - mape: 6.7622 - val_loss: 0.5525 - val_rmse: 0.6416 - val_mape: 2.3450
Epoch 226/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.9199 - rmse: 1.9239 - mape: 7.0663 - val_loss: 0.3944 - val_rmse: 0.5257 - val_mape: 1.9459
Epoch 227/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.3336 - rmse: 1.8540 - mape: 6.8203 - val_loss: 0.7531 - val_rmse: 0.7380 - val_mape: 2.6731
Epoch 228/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.4051 - rmse: 1.8556 - mape: 6.8206 - val_loss: 0.2501 - val_rmse: 0.3900 - val_mape: 1.4149
Epoch 229/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.5961 - rmse: 1.8957 - mape: 6.9679 - val_loss: 0.2064 - val_rmse: 0.3587 - val_mape: 1.3140
Epoch 230/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.5008 - rmse: 1.8757 - mape: 6.8970 - val_loss: 0.3903 - val_rmse: 0.5079 - val_mape: 1.8368
Epoch 231/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.3608 - rmse: 1.8308 - mape: 6.7187 - val_loss: 0.8194 - val_rmse: 0.8065 - val_mape: 2.9346
Epoch 232/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.5421 - rmse: 1.8818 - mape: 6.9036 - val_loss: 0.3308 - val_rmse: 0.4411 - val_mape: 1.5851
Epoch 233/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.5202 - rmse: 1.8473 - mape: 6.7851 - val_loss: 0.1954 - val_rmse: 0.3495 - val_mape: 1.2750
Epoch 234/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.4648 - rmse: 1.8613 - mape: 6.8307 - val_loss: 0.1945 - val_rmse: 0.3520 - val_mape: 1.2828
Epoch 235/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.2383 - rmse: 1.8265 - mape: 6.7077 - val_loss: 0.4088 - val_rmse: 0.5169 - val_mape: 1.8667
Epoch 236/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.2109 - rmse: 1.8219 - mape: 6.6991 - val_loss: 0.3036 - val_rmse: 0.4283 - val_mape: 1.5457
Epoch 237/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.2773 - rmse: 1.8474 - mape: 6.7864 - val_loss: 0.4740 - val_rmse: 0.5621 - val_mape: 2.0314
Epoch 238/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.1519 - rmse: 1.8035 - mape: 6.6197 - val_loss: 0.3525 - val_rmse: 0.4768 - val_mape: 1.7294
Epoch 239/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.2839 - rmse: 1.8319 - mape: 6.7259 - val_loss: 0.3711 - val_rmse: 0.4803 - val_mape: 1.7298
Epoch 240/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.4535 - rmse: 1.8768 - mape: 6.9136 - val_loss: 0.3633 - val_rmse: 0.4831 - val_mape: 1.7501
Epoch 241/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.2245 - rmse: 1.8317 - mape: 6.7394 - val_loss: 0.3035 - val_rmse: 0.4366 - val_mape: 1.5812
Epoch 242/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.1159 - rmse: 1.8117 - mape: 6.6614 - val_loss: 0.2279 - val_rmse: 0.3660 - val_mape: 1.3250
Epoch 243/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.9866 - rmse: 1.7866 - mape: 6.5584 - val_loss: 0.7920 - val_rmse: 0.7807 - val_mape: 2.8415
Epoch 244/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.1595 - rmse: 1.8284 - mape: 6.7156 - val_loss: 0.4841 - val_rmse: 0.5877 - val_mape: 2.1368
Epoch 245/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.3660 - rmse: 1.8434 - mape: 6.7613 - val_loss: 0.1972 - val_rmse: 0.3395 - val_mape: 1.2315
Epoch 246/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.3666 - rmse: 1.8494 - mape: 6.7969 - val_loss: 0.2286 - val_rmse: 0.3972 - val_mape: 1.4668
Epoch 247/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.3196 - rmse: 1.8377 - mape: 6.7506 - val_loss: 0.4489 - val_rmse: 0.5495 - val_mape: 1.9940
Epoch 248/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.0318 - rmse: 1.7874 - mape: 6.5700 - val_loss: 0.3152 - val_rmse: 0.4695 - val_mape: 1.7373
Epoch 249/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.0179 - rmse: 1.7943 - mape: 6.5958 - val_loss: 0.2277 - val_rmse: 0.3708 - val_mape: 1.3602
Epoch 250/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.1555 - rmse: 1.8054 - mape: 6.6302 - val_loss: 0.3223 - val_rmse: 0.4365 - val_mape: 1.5701
Epoch 251/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.0918 - rmse: 1.7926 - mape: 6.5951 - val_loss: 0.3817 - val_rmse: 0.4947 - val_mape: 1.7905
Epoch 252/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.2808 - rmse: 1.8239 - mape: 6.7071 - val_loss: 0.2430 - val_rmse: 0.3926 - val_mape: 1.4354
Epoch 253/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.0338 - rmse: 1.7865 - mape: 6.5591 - val_loss: 0.4378 - val_rmse: 0.5678 - val_mape: 2.1061
Epoch 254/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.9913 - rmse: 1.7771 - mape: 6.5345 - val_loss: 0.2628 - val_rmse: 0.3903 - val_mape: 1.4121
Epoch 255/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.8472 - rmse: 1.7215 - mape: 6.3223 - val_loss: 0.2963 - val_rmse: 0.4236 - val_mape: 1.5348
Epoch 256/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.8620 - rmse: 1.7595 - mape: 6.4676 - val_loss: 0.2080 - val_rmse: 0.3506 - val_mape: 1.2815
Epoch 257/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.1504 - rmse: 1.8138 - mape: 6.6670 - val_loss: 0.2019 - val_rmse: 0.3525 - val_mape: 1.2907
Epoch 258/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.9312 - rmse: 1.7920 - mape: 6.5812 - val_loss: 0.2599 - val_rmse: 0.3933 - val_mape: 1.4242
Epoch 259/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.8761 - rmse: 1.7454 - mape: 6.4113 - val_loss: 0.2782 - val_rmse: 0.4223 - val_mape: 1.5384
Epoch 260/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.9970 - rmse: 1.7864 - mape: 6.5691 - val_loss: 0.6770 - val_rmse: 0.6802 - val_mape: 2.4393
Epoch 261/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.6962 - rmse: 1.7312 - mape: 6.3640 - val_loss: 0.6766 - val_rmse: 0.7174 - val_mape: 2.6181
Epoch 262/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.7710 - rmse: 1.7396 - mape: 6.3924 - val_loss: 1.2073 - val_rmse: 0.9942 - val_mape: 3.6012
Epoch 263/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.1504 - rmse: 1.8133 - mape: 6.6702 - val_loss: 0.3193 - val_rmse: 0.4336 - val_mape: 1.5668
Epoch 264/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.9405 - rmse: 1.7664 - mape: 6.4953 - val_loss: 0.2192 - val_rmse: 0.3681 - val_mape: 1.3427
Epoch 265/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.6965 - rmse: 1.7107 - mape: 6.2789 - val_loss: 0.5106 - val_rmse: 0.5969 - val_mape: 2.1647
Epoch 266/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.9388 - rmse: 1.7596 - mape: 6.4655 - val_loss: 0.2856 - val_rmse: 0.4154 - val_mape: 1.5000
Epoch 267/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.6764 - rmse: 1.7159 - mape: 6.3102 - val_loss: 0.3811 - val_rmse: 0.5092 - val_mape: 1.8514
Epoch 268/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.8769 - rmse: 1.7568 - mape: 6.4706 - val_loss: 0.2450 - val_rmse: 0.3763 - val_mape: 1.3623
Epoch 269/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.7715 - rmse: 1.7489 - mape: 6.4238 - val_loss: 0.2166 - val_rmse: 0.3754 - val_mape: 1.3837
Epoch 270/1000
75/75 [==============================] - 0s 5ms/step - loss: 5.0145 - rmse: 1.7732 - mape: 6.5130 - val_loss: 0.1913 - val_rmse: 0.3434 - val_mape: 1.2522
Epoch 271/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.6335 - rmse: 1.7077 - mape: 6.2864 - val_loss: 0.1957 - val_rmse: 0.3455 - val_mape: 1.2601
Epoch 272/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.7681 - rmse: 1.7225 - mape: 6.3197 - val_loss: 0.3761 - val_rmse: 0.4864 - val_mape: 1.7651
Epoch 273/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.8039 - rmse: 1.7556 - mape: 6.4495 - val_loss: 0.3135 - val_rmse: 0.4378 - val_mape: 1.5893
Epoch 274/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.6776 - rmse: 1.7368 - mape: 6.3814 - val_loss: 0.2910 - val_rmse: 0.4206 - val_mape: 1.5188
Epoch 275/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.8291 - rmse: 1.7367 - mape: 6.3837 - val_loss: 0.2032 - val_rmse: 0.3455 - val_mape: 1.2589
Epoch 276/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.6489 - rmse: 1.7040 - mape: 6.2568 - val_loss: 0.2592 - val_rmse: 0.3883 - val_mape: 1.4051
Epoch 277/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.6097 - rmse: 1.7080 - mape: 6.2816 - val_loss: 0.3677 - val_rmse: 0.4834 - val_mape: 1.7520
Epoch 278/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.6015 - rmse: 1.7109 - mape: 6.2868 - val_loss: 0.2220 - val_rmse: 0.3822 - val_mape: 1.4084
Epoch 279/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.6842 - rmse: 1.7138 - mape: 6.2933 - val_loss: 0.1898 - val_rmse: 0.3409 - val_mape: 1.2509
Epoch 280/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.5442 - rmse: 1.7001 - mape: 6.2393 - val_loss: 0.3621 - val_rmse: 0.4864 - val_mape: 1.7648
Epoch 281/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.6660 - rmse: 1.7368 - mape: 6.3830 - val_loss: 0.7710 - val_rmse: 0.7539 - val_mape: 2.7291
Epoch 282/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.7106 - rmse: 1.7334 - mape: 6.3729 - val_loss: 0.4127 - val_rmse: 0.5109 - val_mape: 1.8447
Epoch 283/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.5107 - rmse: 1.6889 - mape: 6.2059 - val_loss: 0.3688 - val_rmse: 0.4973 - val_mape: 1.8300
Epoch 284/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.4796 - rmse: 1.6919 - mape: 6.2126 - val_loss: 0.5147 - val_rmse: 0.6077 - val_mape: 2.2070
Epoch 285/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.4795 - rmse: 1.6834 - mape: 6.1759 - val_loss: 0.2426 - val_rmse: 0.3879 - val_mape: 1.4160
Epoch 286/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.4315 - rmse: 1.6681 - mape: 6.1268 - val_loss: 0.5290 - val_rmse: 0.6338 - val_mape: 2.3507
Epoch 287/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.6216 - rmse: 1.7192 - mape: 6.3209 - val_loss: 0.2409 - val_rmse: 0.3790 - val_mape: 1.3728
Epoch 288/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.5077 - rmse: 1.7016 - mape: 6.2452 - val_loss: 0.3359 - val_rmse: 0.4533 - val_mape: 1.6367
Epoch 289/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.4410 - rmse: 1.6770 - mape: 6.1663 - val_loss: 0.1862 - val_rmse: 0.3421 - val_mape: 1.2484
Epoch 290/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.5530 - rmse: 1.6871 - mape: 6.1920 - val_loss: 0.3397 - val_rmse: 0.4663 - val_mape: 1.6999
Epoch 291/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.3776 - rmse: 1.6796 - mape: 6.1631 - val_loss: 0.2125 - val_rmse: 0.3488 - val_mape: 1.2667
Epoch 292/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.5327 - rmse: 1.6905 - mape: 6.2082 - val_loss: 0.4115 - val_rmse: 0.5048 - val_mape: 1.8224
Epoch 293/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.2376 - rmse: 1.6505 - mape: 6.0709 - val_loss: 0.8572 - val_rmse: 0.8405 - val_mape: 3.0721
Epoch 294/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.6128 - rmse: 1.7186 - mape: 6.3195 - val_loss: 0.4074 - val_rmse: 0.5250 - val_mape: 1.9186
Epoch 295/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.3031 - rmse: 1.6270 - mape: 5.9738 - val_loss: 0.2938 - val_rmse: 0.4383 - val_mape: 1.6143
Epoch 296/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.3795 - rmse: 1.6664 - mape: 6.1177 - val_loss: 0.1957 - val_rmse: 0.3404 - val_mape: 1.2336
Epoch 297/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.5819 - rmse: 1.6936 - mape: 6.2202 - val_loss: 0.2520 - val_rmse: 0.3846 - val_mape: 1.3886
Epoch 298/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.5675 - rmse: 1.6996 - mape: 6.2314 - val_loss: 0.7291 - val_rmse: 0.7418 - val_mape: 2.6920
Epoch 299/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.3430 - rmse: 1.6636 - mape: 6.1098 - val_loss: 0.1822 - val_rmse: 0.3359 - val_mape: 1.2283
Epoch 300/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.3334 - rmse: 1.6560 - mape: 6.0759 - val_loss: 0.4138 - val_rmse: 0.5183 - val_mape: 1.8663
Epoch 301/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.0952 - rmse: 1.6140 - mape: 5.9298 - val_loss: 0.7847 - val_rmse: 0.7914 - val_mape: 2.8924
Epoch 302/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.2528 - rmse: 1.6326 - mape: 5.9871 - val_loss: 0.3082 - val_rmse: 0.4432 - val_mape: 1.6074
Epoch 303/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.5112 - rmse: 1.7118 - mape: 6.3042 - val_loss: 0.1948 - val_rmse: 0.3419 - val_mape: 1.2416
Epoch 304/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.3288 - rmse: 1.6640 - mape: 6.1178 - val_loss: 0.6336 - val_rmse: 0.6816 - val_mape: 2.4913
Epoch 305/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.3364 - rmse: 1.6612 - mape: 6.1030 - val_loss: 0.2659 - val_rmse: 0.4110 - val_mape: 1.4999
Epoch 306/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.4276 - rmse: 1.6908 - mape: 6.2173 - val_loss: 0.2100 - val_rmse: 0.3627 - val_mape: 1.3237
Epoch 307/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.3329 - rmse: 1.6577 - mape: 6.0882 - val_loss: 0.2361 - val_rmse: 0.3978 - val_mape: 1.4658
Epoch 308/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.0315 - rmse: 1.5980 - mape: 5.8724 - val_loss: 0.6947 - val_rmse: 0.7240 - val_mape: 2.6284
Epoch 309/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.2573 - rmse: 1.6395 - mape: 6.0222 - val_loss: 0.2066 - val_rmse: 0.3501 - val_mape: 1.2675
Epoch 310/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.2140 - rmse: 1.6139 - mape: 5.9277 - val_loss: 0.3256 - val_rmse: 0.4434 - val_mape: 1.5980
Epoch 311/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.3455 - rmse: 1.6564 - mape: 6.0890 - val_loss: 0.8699 - val_rmse: 0.8322 - val_mape: 3.0200
Epoch 312/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.9006 - rmse: 1.5727 - mape: 5.7820 - val_loss: 0.2192 - val_rmse: 0.3638 - val_mape: 1.3197
Epoch 313/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.5334 - rmse: 1.7052 - mape: 6.2674 - val_loss: 0.2871 - val_rmse: 0.4243 - val_mape: 1.5423
Epoch 314/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.1059 - rmse: 1.6239 - mape: 5.9617 - val_loss: 0.2526 - val_rmse: 0.4023 - val_mape: 1.4605
Epoch 315/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.3290 - rmse: 1.6553 - mape: 6.0775 - val_loss: 0.6939 - val_rmse: 0.7401 - val_mape: 2.7071
Epoch 316/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.2245 - rmse: 1.6304 - mape: 5.9879 - val_loss: 0.1837 - val_rmse: 0.3410 - val_mape: 1.2479
Epoch 317/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.2875 - rmse: 1.6370 - mape: 5.9983 - val_loss: 0.2380 - val_rmse: 0.3758 - val_mape: 1.3595
Epoch 318/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.9872 - rmse: 1.5946 - mape: 5.8629 - val_loss: 0.3987 - val_rmse: 0.5310 - val_mape: 1.9733
Epoch 319/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.0165 - rmse: 1.5912 - mape: 5.8628 - val_loss: 0.2530 - val_rmse: 0.3984 - val_mape: 1.4560
Epoch 320/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.0289 - rmse: 1.5972 - mape: 5.8695 - val_loss: 0.5486 - val_rmse: 0.6291 - val_mape: 2.3017
Epoch 321/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.0267 - rmse: 1.6082 - mape: 5.9100 - val_loss: 0.3610 - val_rmse: 0.4699 - val_mape: 1.6991
Epoch 322/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.8730 - rmse: 1.5619 - mape: 5.7396 - val_loss: 0.1956 - val_rmse: 0.3476 - val_mape: 1.2667
Epoch 323/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.2529 - rmse: 1.6532 - mape: 6.0777 - val_loss: 0.2260 - val_rmse: 0.3630 - val_mape: 1.3130
Epoch 324/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.1432 - rmse: 1.6188 - mape: 5.9508 - val_loss: 0.3715 - val_rmse: 0.4920 - val_mape: 1.7909
Epoch 325/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.9912 - rmse: 1.5962 - mape: 5.8663 - val_loss: 0.2179 - val_rmse: 0.3635 - val_mape: 1.3235
Epoch 326/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.9405 - rmse: 1.5841 - mape: 5.8231 - val_loss: 0.3369 - val_rmse: 0.4661 - val_mape: 1.6876
Epoch 327/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.9910 - rmse: 1.5952 - mape: 5.8581 - val_loss: 0.4240 - val_rmse: 0.5304 - val_mape: 1.9172
Epoch 328/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.7742 - rmse: 1.5346 - mape: 5.6394 - val_loss: 0.3964 - val_rmse: 0.5102 - val_mape: 1.8418
Epoch 329/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.0215 - rmse: 1.6039 - mape: 5.8895 - val_loss: 0.1875 - val_rmse: 0.3408 - val_mape: 1.2417
Epoch 330/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.9630 - rmse: 1.5770 - mape: 5.7878 - val_loss: 0.1978 - val_rmse: 0.3434 - val_mape: 1.2538
Epoch 331/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.8883 - rmse: 1.5617 - mape: 5.7406 - val_loss: 0.2395 - val_rmse: 0.3775 - val_mape: 1.3643
Epoch 332/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.1306 - rmse: 1.6070 - mape: 5.8914 - val_loss: 0.2782 - val_rmse: 0.4273 - val_mape: 1.5769
Epoch 333/1000
75/75 [==============================] - 0s 5ms/step - loss: 4.2184 - rmse: 1.6539 - mape: 6.0777 - val_loss: 0.2156 - val_rmse: 0.3543 - val_mape: 1.2817
Epoch 334/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.9988 - rmse: 1.5741 - mape: 5.7806 - val_loss: 0.7788 - val_rmse: 0.7830 - val_mape: 2.8666
Epoch 335/1000
75/75 [==============================] - 1s 7ms/step - loss: 3.9659 - rmse: 1.5889 - mape: 5.8351 - val_loss: 0.1893 - val_rmse: 0.3413 - val_mape: 1.2506
Epoch 336/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.9728 - rmse: 1.5884 - mape: 5.8331 - val_loss: 0.1843 - val_rmse: 0.3296 - val_mape: 1.1997
Epoch 337/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.8334 - rmse: 1.5599 - mape: 5.7399 - val_loss: 0.2304 - val_rmse: 0.3732 - val_mape: 1.3594
Epoch 338/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.9372 - rmse: 1.5792 - mape: 5.8045 - val_loss: 0.6789 - val_rmse: 0.7278 - val_mape: 2.7065
Epoch 339/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.8843 - rmse: 1.5601 - mape: 5.7216 - val_loss: 0.2823 - val_rmse: 0.4156 - val_mape: 1.5031
Epoch 340/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.8988 - rmse: 1.5791 - mape: 5.8003 - val_loss: 0.1922 - val_rmse: 0.3510 - val_mape: 1.2904
Epoch 341/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.6949 - rmse: 1.5382 - mape: 5.6468 - val_loss: 0.2268 - val_rmse: 0.3706 - val_mape: 1.3433
Epoch 342/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.9181 - rmse: 1.5701 - mape: 5.7592 - val_loss: 0.2230 - val_rmse: 0.3794 - val_mape: 1.3946
Epoch 343/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.7722 - rmse: 1.5563 - mape: 5.7111 - val_loss: 0.2352 - val_rmse: 0.3833 - val_mape: 1.3963
Epoch 344/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.6885 - rmse: 1.5199 - mape: 5.5762 - val_loss: 0.2577 - val_rmse: 0.4019 - val_mape: 1.4639
Epoch 345/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.9234 - rmse: 1.5840 - mape: 5.8094 - val_loss: 0.2740 - val_rmse: 0.4126 - val_mape: 1.4971
Epoch 346/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.9652 - rmse: 1.5904 - mape: 5.8271 - val_loss: 0.2287 - val_rmse: 0.3684 - val_mape: 1.3359
Epoch 347/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.6349 - rmse: 1.5220 - mape: 5.5846 - val_loss: 0.2141 - val_rmse: 0.3679 - val_mape: 1.3404
Epoch 348/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.6425 - rmse: 1.5250 - mape: 5.5928 - val_loss: 0.2780 - val_rmse: 0.4156 - val_mape: 1.5068
Epoch 349/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.8221 - rmse: 1.5610 - mape: 5.7382 - val_loss: 0.2141 - val_rmse: 0.3749 - val_mape: 1.3770
Epoch 350/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.8074 - rmse: 1.5519 - mape: 5.6971 - val_loss: 0.4778 - val_rmse: 0.5747 - val_mape: 2.0861
Epoch 351/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.8865 - rmse: 1.5772 - mape: 5.7953 - val_loss: 0.2428 - val_rmse: 0.3815 - val_mape: 1.3855
Epoch 352/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.7290 - rmse: 1.5491 - mape: 5.6871 - val_loss: 0.3312 - val_rmse: 0.4566 - val_mape: 1.6563
Epoch 353/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.6592 - rmse: 1.5290 - mape: 5.6252 - val_loss: 0.4228 - val_rmse: 0.5423 - val_mape: 1.9797
Epoch 354/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.5811 - rmse: 1.5075 - mape: 5.5364 - val_loss: 0.2146 - val_rmse: 0.3604 - val_mape: 1.3198
Epoch 355/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.3603 - rmse: 1.4474 - mape: 5.3182 - val_loss: 0.3609 - val_rmse: 0.4834 - val_mape: 1.7468
Epoch 356/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.6892 - rmse: 1.5298 - mape: 5.6148 - val_loss: 0.1798 - val_rmse: 0.3313 - val_mape: 1.2099
Epoch 357/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.5358 - rmse: 1.4937 - mape: 5.4873 - val_loss: 0.1850 - val_rmse: 0.3376 - val_mape: 1.2345
Epoch 358/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.7690 - rmse: 1.5345 - mape: 5.6282 - val_loss: 0.5652 - val_rmse: 0.6368 - val_mape: 2.3098
Epoch 359/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.7107 - rmse: 1.5260 - mape: 5.6034 - val_loss: 0.5907 - val_rmse: 0.6698 - val_mape: 2.4471
Epoch 360/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.5740 - rmse: 1.4968 - mape: 5.4992 - val_loss: 0.1846 - val_rmse: 0.3326 - val_mape: 1.2098
Epoch 361/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.5322 - rmse: 1.5007 - mape: 5.5128 - val_loss: 0.1759 - val_rmse: 0.3364 - val_mape: 1.2366
Epoch 362/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.5139 - rmse: 1.4886 - mape: 5.4636 - val_loss: 0.1749 - val_rmse: 0.3287 - val_mape: 1.2004
Epoch 363/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.4583 - rmse: 1.4731 - mape: 5.4079 - val_loss: 0.2208 - val_rmse: 0.3609 - val_mape: 1.3058
Epoch 364/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.4744 - rmse: 1.4831 - mape: 5.4615 - val_loss: 0.2816 - val_rmse: 0.4234 - val_mape: 1.5470
Epoch 365/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.5664 - rmse: 1.5231 - mape: 5.5904 - val_loss: 1.1513 - val_rmse: 0.9869 - val_mape: 3.5901
Epoch 366/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.4976 - rmse: 1.4789 - mape: 5.4290 - val_loss: 0.1776 - val_rmse: 0.3392 - val_mape: 1.2438
Epoch 367/1000
75/75 [==============================] - 0s 6ms/step - loss: 3.5795 - rmse: 1.4907 - mape: 5.4663 - val_loss: 0.3509 - val_rmse: 0.4968 - val_mape: 1.8596
Epoch 368/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.6756 - rmse: 1.5175 - mape: 5.5750 - val_loss: 0.2184 - val_rmse: 0.3822 - val_mape: 1.4065
Epoch 369/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.4248 - rmse: 1.4732 - mape: 5.4146 - val_loss: 0.2817 - val_rmse: 0.4206 - val_mape: 1.5275
Epoch 370/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.4779 - rmse: 1.4888 - mape: 5.4576 - val_loss: 0.1944 - val_rmse: 0.3490 - val_mape: 1.2726
Epoch 371/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.5727 - rmse: 1.5198 - mape: 5.5804 - val_loss: 0.1951 - val_rmse: 0.3496 - val_mape: 1.2835
Epoch 372/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.3717 - rmse: 1.4482 - mape: 5.3135 - val_loss: 0.5533 - val_rmse: 0.6329 - val_mape: 2.3135
Epoch 373/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.4404 - rmse: 1.4708 - mape: 5.4045 - val_loss: 0.1705 - val_rmse: 0.3165 - val_mape: 1.1515
Epoch 374/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.3779 - rmse: 1.4661 - mape: 5.3780 - val_loss: 0.4625 - val_rmse: 0.5736 - val_mape: 2.0937
Epoch 375/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.5295 - rmse: 1.4866 - mape: 5.4574 - val_loss: 0.2568 - val_rmse: 0.3915 - val_mape: 1.4226
Epoch 376/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.2768 - rmse: 1.4345 - mape: 5.2679 - val_loss: 0.1947 - val_rmse: 0.3511 - val_mape: 1.2850
Epoch 377/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.4690 - rmse: 1.4837 - mape: 5.4492 - val_loss: 0.4342 - val_rmse: 0.5109 - val_mape: 1.8299
Epoch 378/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.2941 - rmse: 1.4373 - mape: 5.2808 - val_loss: 0.1742 - val_rmse: 0.3310 - val_mape: 1.2101
Epoch 379/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.3654 - rmse: 1.4468 - mape: 5.3143 - val_loss: 0.1992 - val_rmse: 0.3518 - val_mape: 1.2849
Epoch 380/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.5009 - rmse: 1.4778 - mape: 5.4229 - val_loss: 0.1790 - val_rmse: 0.3268 - val_mape: 1.1865
Epoch 381/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.3900 - rmse: 1.4619 - mape: 5.3693 - val_loss: 0.2734 - val_rmse: 0.4130 - val_mape: 1.4978
Epoch 382/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.3491 - rmse: 1.4703 - mape: 5.4008 - val_loss: 0.1892 - val_rmse: 0.3479 - val_mape: 1.2746
Epoch 383/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.4604 - rmse: 1.4862 - mape: 5.4594 - val_loss: 0.6830 - val_rmse: 0.7259 - val_mape: 2.6404
Epoch 384/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.1859 - rmse: 1.4149 - mape: 5.1935 - val_loss: 0.4292 - val_rmse: 0.5470 - val_mape: 2.0070
Epoch 385/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.4696 - rmse: 1.4860 - mape: 5.4631 - val_loss: 0.1982 - val_rmse: 0.3425 - val_mape: 1.2408
Epoch 386/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.3645 - rmse: 1.4619 - mape: 5.3720 - val_loss: 0.1759 - val_rmse: 0.3353 - val_mape: 1.2294
Epoch 387/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.1334 - rmse: 1.4037 - mape: 5.1555 - val_loss: 0.2546 - val_rmse: 0.3987 - val_mape: 1.4530
Epoch 388/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.3000 - rmse: 1.4528 - mape: 5.3323 - val_loss: 0.2231 - val_rmse: 0.3687 - val_mape: 1.3472
Epoch 389/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.3222 - rmse: 1.4539 - mape: 5.3448 - val_loss: 0.1864 - val_rmse: 0.3308 - val_mape: 1.2038
Epoch 390/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.3921 - rmse: 1.4907 - mape: 5.4777 - val_loss: 0.8842 - val_rmse: 0.8322 - val_mape: 3.0126
Epoch 391/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.3891 - rmse: 1.4722 - mape: 5.4054 - val_loss: 0.1951 - val_rmse: 0.3425 - val_mape: 1.2448
Epoch 392/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.2076 - rmse: 1.4225 - mape: 5.2209 - val_loss: 0.2395 - val_rmse: 0.3728 - val_mape: 1.3522
Epoch 393/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.3415 - rmse: 1.4544 - mape: 5.3408 - val_loss: 0.3442 - val_rmse: 0.4672 - val_mape: 1.6850
Epoch 394/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.2080 - rmse: 1.4360 - mape: 5.2819 - val_loss: 0.2023 - val_rmse: 0.3439 - val_mape: 1.2434
Epoch 395/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.3782 - rmse: 1.4641 - mape: 5.3782 - val_loss: 0.2509 - val_rmse: 0.3998 - val_mape: 1.4549
Epoch 396/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.1173 - rmse: 1.4195 - mape: 5.2114 - val_loss: 0.2083 - val_rmse: 0.3556 - val_mape: 1.2863
Epoch 397/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.2631 - rmse: 1.4421 - mape: 5.2957 - val_loss: 0.5458 - val_rmse: 0.6252 - val_mape: 2.2627
Epoch 398/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.1842 - rmse: 1.4205 - mape: 5.2151 - val_loss: 0.1991 - val_rmse: 0.3441 - val_mape: 1.2452
Epoch 399/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.0944 - rmse: 1.4002 - mape: 5.1345 - val_loss: 0.1958 - val_rmse: 0.3457 - val_mape: 1.2560
Epoch 400/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.9032 - rmse: 1.3486 - mape: 4.9570 - val_loss: 0.1830 - val_rmse: 0.3318 - val_mape: 1.2109
Epoch 401/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.1544 - rmse: 1.4169 - mape: 5.2047 - val_loss: 0.1959 - val_rmse: 0.3497 - val_mape: 1.2733
Epoch 402/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.0570 - rmse: 1.3915 - mape: 5.1047 - val_loss: 0.1991 - val_rmse: 0.3414 - val_mape: 1.2376
Epoch 403/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.2393 - rmse: 1.4211 - mape: 5.2128 - val_loss: 0.6304 - val_rmse: 0.6874 - val_mape: 2.5092
Epoch 404/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.0481 - rmse: 1.4039 - mape: 5.1585 - val_loss: 0.2027 - val_rmse: 0.3518 - val_mape: 1.2802
Epoch 405/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.1566 - rmse: 1.4227 - mape: 5.2186 - val_loss: 0.6199 - val_rmse: 0.6617 - val_mape: 2.3945
Epoch 406/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.1172 - rmse: 1.4008 - mape: 5.1402 - val_loss: 0.2088 - val_rmse: 0.3524 - val_mape: 1.2854
Epoch 407/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.1885 - rmse: 1.4284 - mape: 5.2424 - val_loss: 0.6512 - val_rmse: 0.6978 - val_mape: 2.5381
Epoch 408/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.0656 - rmse: 1.3924 - mape: 5.1092 - val_loss: 0.4128 - val_rmse: 0.5286 - val_mape: 1.9223
Epoch 409/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.0382 - rmse: 1.3811 - mape: 5.0769 - val_loss: 0.2026 - val_rmse: 0.3477 - val_mape: 1.2634
Epoch 410/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.1436 - rmse: 1.4109 - mape: 5.1744 - val_loss: 0.1981 - val_rmse: 0.3543 - val_mape: 1.2957
Epoch 411/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.0324 - rmse: 1.4014 - mape: 5.1427 - val_loss: 0.1894 - val_rmse: 0.3333 - val_mape: 1.2090
Epoch 412/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.9325 - rmse: 1.3565 - mape: 4.9777 - val_loss: 0.2297 - val_rmse: 0.3742 - val_mape: 1.3581
Epoch 413/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.8180 - rmse: 1.3457 - mape: 4.9424 - val_loss: 0.3656 - val_rmse: 0.4880 - val_mape: 1.7736
Epoch 414/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.2673 - rmse: 1.4269 - mape: 5.2347 - val_loss: 0.2066 - val_rmse: 0.3486 - val_mape: 1.2690
Epoch 415/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.0102 - rmse: 1.3840 - mape: 5.0785 - val_loss: 0.3534 - val_rmse: 0.4901 - val_mape: 1.7972
Epoch 416/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.0466 - rmse: 1.3918 - mape: 5.1049 - val_loss: 0.8568 - val_rmse: 0.8306 - val_mape: 3.0582
Epoch 417/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.0553 - rmse: 1.3830 - mape: 5.0735 - val_loss: 0.2877 - val_rmse: 0.4227 - val_mape: 1.5376
Epoch 418/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.8787 - rmse: 1.3456 - mape: 4.9366 - val_loss: 0.7654 - val_rmse: 0.7695 - val_mape: 2.8629
Epoch 419/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.8786 - rmse: 1.3615 - mape: 4.9957 - val_loss: 0.2109 - val_rmse: 0.3576 - val_mape: 1.2983
Epoch 420/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.0306 - rmse: 1.3729 - mape: 5.0380 - val_loss: 0.2788 - val_rmse: 0.4171 - val_mape: 1.5070
Epoch 421/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.8320 - rmse: 1.3321 - mape: 4.8899 - val_loss: 0.2992 - val_rmse: 0.4346 - val_mape: 1.5710
Epoch 422/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.9494 - rmse: 1.3609 - mape: 4.9903 - val_loss: 0.1783 - val_rmse: 0.3299 - val_mape: 1.2035
Epoch 423/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.8427 - rmse: 1.3396 - mape: 4.9185 - val_loss: 0.2487 - val_rmse: 0.3960 - val_mape: 1.4443
Epoch 424/1000
75/75 [==============================] - 0s 5ms/step - loss: 3.0283 - rmse: 1.3836 - mape: 5.0799 - val_loss: 0.1789 - val_rmse: 0.3227 - val_mape: 1.1738
Epoch 425/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.8067 - rmse: 1.3400 - mape: 4.9167 - val_loss: 0.2268 - val_rmse: 0.3775 - val_mape: 1.3768
Epoch 426/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.9039 - rmse: 1.3491 - mape: 4.9496 - val_loss: 0.2040 - val_rmse: 0.3475 - val_mape: 1.2627
Epoch 427/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.7377 - rmse: 1.3231 - mape: 4.8572 - val_loss: 0.2737 - val_rmse: 0.4092 - val_mape: 1.4776
Epoch 428/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.8379 - rmse: 1.3390 - mape: 4.9211 - val_loss: 0.2564 - val_rmse: 0.4066 - val_mape: 1.5037
Epoch 429/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.9374 - rmse: 1.3728 - mape: 5.0372 - val_loss: 0.2076 - val_rmse: 0.3535 - val_mape: 1.2971
Epoch 430/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.6973 - rmse: 1.3018 - mape: 4.7676 - val_loss: 0.1708 - val_rmse: 0.3168 - val_mape: 1.1580
Epoch 431/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.7133 - rmse: 1.2950 - mape: 4.7468 - val_loss: 0.2266 - val_rmse: 0.3672 - val_mape: 1.3466
Epoch 432/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.8102 - rmse: 1.3450 - mape: 4.9402 - val_loss: 0.2491 - val_rmse: 0.3746 - val_mape: 1.3508
Epoch 433/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.6804 - rmse: 1.2995 - mape: 4.7701 - val_loss: 0.4629 - val_rmse: 0.5577 - val_mape: 2.0146
Epoch 434/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.8068 - rmse: 1.3246 - mape: 4.8548 - val_loss: 0.2622 - val_rmse: 0.3943 - val_mape: 1.4243
Epoch 435/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.7734 - rmse: 1.3288 - mape: 4.8769 - val_loss: 0.4277 - val_rmse: 0.5309 - val_mape: 1.9191
Epoch 436/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.7618 - rmse: 1.3244 - mape: 4.8645 - val_loss: 0.1968 - val_rmse: 0.3491 - val_mape: 1.2776
Epoch 437/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.7229 - rmse: 1.3058 - mape: 4.7926 - val_loss: 0.2762 - val_rmse: 0.4075 - val_mape: 1.4688
Epoch 438/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.8261 - rmse: 1.3284 - mape: 4.8710 - val_loss: 0.1840 - val_rmse: 0.3260 - val_mape: 1.1815
Epoch 439/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.6604 - rmse: 1.2925 - mape: 4.7473 - val_loss: 0.1802 - val_rmse: 0.3268 - val_mape: 1.1876
Epoch 440/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.8393 - rmse: 1.3269 - mape: 4.8693 - val_loss: 0.2673 - val_rmse: 0.4086 - val_mape: 1.4825
Epoch 441/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.7873 - rmse: 1.3297 - mape: 4.8826 - val_loss: 0.2531 - val_rmse: 0.4004 - val_mape: 1.4511
Epoch 442/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.7725 - rmse: 1.3182 - mape: 4.8400 - val_loss: 0.5798 - val_rmse: 0.6574 - val_mape: 2.3860
Epoch 443/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.6350 - rmse: 1.2961 - mape: 4.7559 - val_loss: 0.4110 - val_rmse: 0.5312 - val_mape: 1.9336
Epoch 444/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.6987 - rmse: 1.2916 - mape: 4.7288 - val_loss: 0.3317 - val_rmse: 0.4576 - val_mape: 1.6544
Epoch 445/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.6740 - rmse: 1.3054 - mape: 4.7906 - val_loss: 0.3816 - val_rmse: 0.4999 - val_mape: 1.8033
Epoch 446/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.6634 - rmse: 1.2967 - mape: 4.7657 - val_loss: 0.2161 - val_rmse: 0.3597 - val_mape: 1.3041
Epoch 447/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.5611 - rmse: 1.2758 - mape: 4.6814 - val_loss: 0.5010 - val_rmse: 0.6085 - val_mape: 2.2236
Epoch 448/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.7805 - rmse: 1.3248 - mape: 4.8646 - val_loss: 0.2112 - val_rmse: 0.3669 - val_mape: 1.3360
Epoch 449/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.6437 - rmse: 1.2812 - mape: 4.6992 - val_loss: 0.1913 - val_rmse: 0.3486 - val_mape: 1.2755
Epoch 450/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.7043 - rmse: 1.3037 - mape: 4.7805 - val_loss: 0.2713 - val_rmse: 0.4195 - val_mape: 1.5258
Epoch 451/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.6613 - rmse: 1.2904 - mape: 4.7342 - val_loss: 0.3189 - val_rmse: 0.4563 - val_mape: 1.6554
Epoch 452/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.5891 - rmse: 1.2822 - mape: 4.7060 - val_loss: 0.1709 - val_rmse: 0.3228 - val_mape: 1.1770
Epoch 453/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.7035 - rmse: 1.3291 - mape: 4.8699 - val_loss: 0.1733 - val_rmse: 0.3274 - val_mape: 1.1979
Epoch 454/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.6699 - rmse: 1.2924 - mape: 4.7379 - val_loss: 0.2220 - val_rmse: 0.3612 - val_mape: 1.3016
Epoch 455/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.7107 - rmse: 1.3009 - mape: 4.7751 - val_loss: 0.5311 - val_rmse: 0.6229 - val_mape: 2.2788
Epoch 456/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.5696 - rmse: 1.2816 - mape: 4.7013 - val_loss: 0.2170 - val_rmse: 0.3604 - val_mape: 1.3073
Epoch 457/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.5151 - rmse: 1.2638 - mape: 4.6371 - val_loss: 0.2269 - val_rmse: 0.3723 - val_mape: 1.3554
Epoch 458/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.4775 - rmse: 1.2556 - mape: 4.6050 - val_loss: 0.1761 - val_rmse: 0.3278 - val_mape: 1.1983
Epoch 459/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.5594 - rmse: 1.2856 - mape: 4.7204 - val_loss: 0.2845 - val_rmse: 0.4184 - val_mape: 1.5101
Epoch 460/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.4256 - rmse: 1.2321 - mape: 4.5143 - val_loss: 0.3225 - val_rmse: 0.4521 - val_mape: 1.6377
Epoch 461/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.5183 - rmse: 1.2555 - mape: 4.6152 - val_loss: 0.1956 - val_rmse: 0.3408 - val_mape: 1.2393
Epoch 462/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.4521 - rmse: 1.2424 - mape: 4.5640 - val_loss: 0.3938 - val_rmse: 0.5047 - val_mape: 1.8213
Epoch 463/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.4866 - rmse: 1.2580 - mape: 4.6131 - val_loss: 0.2850 - val_rmse: 0.4109 - val_mape: 1.4831
Epoch 464/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.5550 - rmse: 1.2725 - mape: 4.6661 - val_loss: 0.3607 - val_rmse: 0.4692 - val_mape: 1.6888
Epoch 465/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.5604 - rmse: 1.2690 - mape: 4.6556 - val_loss: 0.2666 - val_rmse: 0.4032 - val_mape: 1.4598
Epoch 466/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.5063 - rmse: 1.2628 - mape: 4.6348 - val_loss: 0.1929 - val_rmse: 0.3560 - val_mape: 1.3048
Epoch 467/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.4589 - rmse: 1.2524 - mape: 4.5929 - val_loss: 0.2197 - val_rmse: 0.3814 - val_mape: 1.4028
Epoch 468/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.5170 - rmse: 1.2524 - mape: 4.5986 - val_loss: 0.2084 - val_rmse: 0.3483 - val_mape: 1.2611
Epoch 469/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.4480 - rmse: 1.2405 - mape: 4.5445 - val_loss: 0.5655 - val_rmse: 0.6424 - val_mape: 2.3341
Epoch 470/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.5141 - rmse: 1.2537 - mape: 4.5988 - val_loss: 0.2554 - val_rmse: 0.4031 - val_mape: 1.4694
Epoch 471/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.3968 - rmse: 1.2311 - mape: 4.5213 - val_loss: 0.1869 - val_rmse: 0.3341 - val_mape: 1.2133
Epoch 472/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.4340 - rmse: 1.2452 - mape: 4.5699 - val_loss: 0.2542 - val_rmse: 0.4014 - val_mape: 1.4627
Epoch 473/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.5845 - rmse: 1.2787 - mape: 4.6908 - val_loss: 0.2527 - val_rmse: 0.3944 - val_mape: 1.4276
Epoch 474/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.3905 - rmse: 1.2249 - mape: 4.4903 - val_loss: 0.5136 - val_rmse: 0.6032 - val_mape: 2.1813
Epoch 475/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.4253 - rmse: 1.2327 - mape: 4.5230 - val_loss: 0.2187 - val_rmse: 0.3718 - val_mape: 1.3620
Epoch 476/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.2178 - rmse: 1.1877 - mape: 4.3607 - val_loss: 0.4461 - val_rmse: 0.5558 - val_mape: 2.0148
Epoch 477/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.4285 - rmse: 1.2329 - mape: 4.5181 - val_loss: 0.1837 - val_rmse: 0.3273 - val_mape: 1.1927
Epoch 478/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.2713 - rmse: 1.2106 - mape: 4.4406 - val_loss: 0.2519 - val_rmse: 0.3836 - val_mape: 1.3835
Epoch 479/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.3743 - rmse: 1.2215 - mape: 4.4840 - val_loss: 0.3263 - val_rmse: 0.4578 - val_mape: 1.6602
Epoch 480/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.3847 - rmse: 1.2264 - mape: 4.4992 - val_loss: 0.1739 - val_rmse: 0.3234 - val_mape: 1.1801
Epoch 481/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.2733 - rmse: 1.2048 - mape: 4.4238 - val_loss: 0.2125 - val_rmse: 0.3643 - val_mape: 1.3437
Epoch 482/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.3955 - rmse: 1.2337 - mape: 4.5294 - val_loss: 0.2476 - val_rmse: 0.3927 - val_mape: 1.4224
Epoch 483/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.2167 - rmse: 1.1873 - mape: 4.3536 - val_loss: 0.3080 - val_rmse: 0.4467 - val_mape: 1.6216
Epoch 484/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.1245 - rmse: 1.1579 - mape: 4.2426 - val_loss: 0.1751 - val_rmse: 0.3308 - val_mape: 1.2017
Epoch 485/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.3181 - rmse: 1.2122 - mape: 4.4455 - val_loss: 0.2321 - val_rmse: 0.3750 - val_mape: 1.3557
Epoch 486/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.2402 - rmse: 1.2035 - mape: 4.4172 - val_loss: 0.2504 - val_rmse: 0.3915 - val_mape: 1.4217
Epoch 487/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.2424 - rmse: 1.1821 - mape: 4.3331 - val_loss: 0.2033 - val_rmse: 0.3487 - val_mape: 1.2666
Epoch 488/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.1931 - rmse: 1.1691 - mape: 4.2901 - val_loss: 0.1932 - val_rmse: 0.3437 - val_mape: 1.2565
Epoch 489/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.2462 - rmse: 1.1910 - mape: 4.3623 - val_loss: 0.3423 - val_rmse: 0.4800 - val_mape: 1.7494
Epoch 490/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.2948 - rmse: 1.1954 - mape: 4.3787 - val_loss: 0.2156 - val_rmse: 0.3587 - val_mape: 1.3004
Epoch 491/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.3885 - rmse: 1.2309 - mape: 4.5146 - val_loss: 0.9218 - val_rmse: 0.8616 - val_mape: 3.1240
Epoch 492/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.3226 - rmse: 1.2051 - mape: 4.4228 - val_loss: 0.1998 - val_rmse: 0.3491 - val_mape: 1.2676
Epoch 493/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.1601 - rmse: 1.1745 - mape: 4.3064 - val_loss: 0.1881 - val_rmse: 0.3412 - val_mape: 1.2428
Epoch 494/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.2010 - rmse: 1.1846 - mape: 4.3459 - val_loss: 0.2708 - val_rmse: 0.4100 - val_mape: 1.4848
Epoch 495/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.3039 - rmse: 1.2072 - mape: 4.4246 - val_loss: 0.2009 - val_rmse: 0.3492 - val_mape: 1.2689
Epoch 496/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.2572 - rmse: 1.1966 - mape: 4.3880 - val_loss: 0.2322 - val_rmse: 0.3738 - val_mape: 1.3520
Epoch 497/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.3123 - rmse: 1.2080 - mape: 4.4277 - val_loss: 0.1931 - val_rmse: 0.3520 - val_mape: 1.2936
Epoch 498/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.1741 - rmse: 1.1810 - mape: 4.3306 - val_loss: 0.1978 - val_rmse: 0.3515 - val_mape: 1.2821
Epoch 499/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.1699 - rmse: 1.1749 - mape: 4.3098 - val_loss: 0.2393 - val_rmse: 0.3805 - val_mape: 1.3762
Epoch 500/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.1291 - rmse: 1.1495 - mape: 4.2176 - val_loss: 0.1775 - val_rmse: 0.3276 - val_mape: 1.1891
Epoch 501/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.0355 - rmse: 1.1351 - mape: 4.1616 - val_loss: 0.2121 - val_rmse: 0.3701 - val_mape: 1.3634
Epoch 502/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.2893 - rmse: 1.2010 - mape: 4.4017 - val_loss: 0.1648 - val_rmse: 0.3173 - val_mape: 1.1577
Epoch 503/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.1943 - rmse: 1.1774 - mape: 4.3148 - val_loss: 0.1827 - val_rmse: 0.3369 - val_mape: 1.2294
Epoch 504/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.1402 - rmse: 1.1626 - mape: 4.2631 - val_loss: 0.2863 - val_rmse: 0.4264 - val_mape: 1.5413
Epoch 505/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.1040 - rmse: 1.1451 - mape: 4.1986 - val_loss: 0.1756 - val_rmse: 0.3308 - val_mape: 1.2054
Epoch 506/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.1234 - rmse: 1.1483 - mape: 4.2106 - val_loss: 0.1915 - val_rmse: 0.3440 - val_mape: 1.2547
Epoch 507/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.1618 - rmse: 1.1715 - mape: 4.2993 - val_loss: 0.3047 - val_rmse: 0.4451 - val_mape: 1.6296
Epoch 508/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.1191 - rmse: 1.1463 - mape: 4.2012 - val_loss: 0.2409 - val_rmse: 0.3926 - val_mape: 1.4279
Epoch 509/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.9645 - rmse: 1.1200 - mape: 4.1052 - val_loss: 0.1961 - val_rmse: 0.3448 - val_mape: 1.2568
Epoch 510/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.0192 - rmse: 1.1331 - mape: 4.1612 - val_loss: 0.1872 - val_rmse: 0.3327 - val_mape: 1.2104
Epoch 511/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.0647 - rmse: 1.1452 - mape: 4.1988 - val_loss: 0.3174 - val_rmse: 0.4416 - val_mape: 1.5977
Epoch 512/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.1821 - rmse: 1.1826 - mape: 4.3401 - val_loss: 0.3371 - val_rmse: 0.4609 - val_mape: 1.6643
Epoch 513/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.0628 - rmse: 1.1346 - mape: 4.1593 - val_loss: 0.1853 - val_rmse: 0.3382 - val_mape: 1.2349
Epoch 514/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.9960 - rmse: 1.1308 - mape: 4.1507 - val_loss: 0.1864 - val_rmse: 0.3336 - val_mape: 1.2140
Epoch 515/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.9222 - rmse: 1.1037 - mape: 4.0486 - val_loss: 0.1769 - val_rmse: 0.3260 - val_mape: 1.1859
Epoch 516/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.0280 - rmse: 1.1280 - mape: 4.1337 - val_loss: 0.2295 - val_rmse: 0.3714 - val_mape: 1.3438
Epoch 517/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.9211 - rmse: 1.1006 - mape: 4.0354 - val_loss: 0.1972 - val_rmse: 0.3408 - val_mape: 1.2348
Epoch 518/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.9446 - rmse: 1.1049 - mape: 4.0525 - val_loss: 0.2632 - val_rmse: 0.4063 - val_mape: 1.4742
Epoch 519/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.0461 - rmse: 1.1317 - mape: 4.1513 - val_loss: 0.7797 - val_rmse: 0.7826 - val_mape: 2.8428
Epoch 520/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.9959 - rmse: 1.1165 - mape: 4.0935 - val_loss: 0.2056 - val_rmse: 0.3594 - val_mape: 1.3064
Epoch 521/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.9895 - rmse: 1.1151 - mape: 4.0868 - val_loss: 0.2290 - val_rmse: 0.3660 - val_mape: 1.3194
Epoch 522/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.8500 - rmse: 1.0890 - mape: 3.9955 - val_loss: 0.3758 - val_rmse: 0.5106 - val_mape: 1.8644
Epoch 523/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.0232 - rmse: 1.1243 - mape: 4.1184 - val_loss: 0.2007 - val_rmse: 0.3508 - val_mape: 1.2789
Epoch 524/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.9268 - rmse: 1.1054 - mape: 4.0511 - val_loss: 0.1732 - val_rmse: 0.3246 - val_mape: 1.1850
Epoch 525/1000
75/75 [==============================] - 0s 5ms/step - loss: 2.0260 - rmse: 1.1303 - mape: 4.1406 - val_loss: 0.2135 - val_rmse: 0.3561 - val_mape: 1.2886
Epoch 526/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.9104 - rmse: 1.0935 - mape: 4.0027 - val_loss: 0.2325 - val_rmse: 0.3724 - val_mape: 1.3423
Epoch 527/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.9349 - rmse: 1.0918 - mape: 3.9995 - val_loss: 0.2192 - val_rmse: 0.3679 - val_mape: 1.3349
Epoch 528/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.8764 - rmse: 1.0874 - mape: 3.9826 - val_loss: 0.1801 - val_rmse: 0.3261 - val_mape: 1.1832
Epoch 529/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.8644 - rmse: 1.0880 - mape: 3.9894 - val_loss: 0.2174 - val_rmse: 0.3719 - val_mape: 1.3527
Epoch 530/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.8376 - rmse: 1.0691 - mape: 3.9151 - val_loss: 0.1703 - val_rmse: 0.3257 - val_mape: 1.1885
Epoch 531/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.8224 - rmse: 1.0648 - mape: 3.9034 - val_loss: 0.2019 - val_rmse: 0.3509 - val_mape: 1.2729
Epoch 532/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.8212 - rmse: 1.0742 - mape: 3.9402 - val_loss: 0.2467 - val_rmse: 0.4037 - val_mape: 1.4923
Epoch 533/1000
75/75 [==============================] - 0s 6ms/step - loss: 1.9310 - rmse: 1.1056 - mape: 4.0451 - val_loss: 0.6015 - val_rmse: 0.6682 - val_mape: 2.4188
Epoch 534/1000
75/75 [==============================] - 0s 6ms/step - loss: 1.7283 - rmse: 1.0463 - mape: 3.8384 - val_loss: 0.4333 - val_rmse: 0.5455 - val_mape: 1.9710
Epoch 535/1000
75/75 [==============================] - 0s 6ms/step - loss: 1.8560 - rmse: 1.0769 - mape: 3.9396 - val_loss: 0.2930 - val_rmse: 0.4485 - val_mape: 1.6720
Epoch 536/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.8456 - rmse: 1.0745 - mape: 3.9377 - val_loss: 0.3200 - val_rmse: 0.4594 - val_mape: 1.6690
Epoch 537/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.8049 - rmse: 1.0683 - mape: 3.9169 - val_loss: 0.3243 - val_rmse: 0.4628 - val_mape: 1.6762
Epoch 538/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.8750 - rmse: 1.0789 - mape: 3.9512 - val_loss: 0.4775 - val_rmse: 0.5835 - val_mape: 2.1153
Epoch 539/1000
75/75 [==============================] - 0s 6ms/step - loss: 1.7989 - rmse: 1.0614 - mape: 3.8924 - val_loss: 0.2009 - val_rmse: 0.3498 - val_mape: 1.2824
Epoch 540/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.8397 - rmse: 1.0761 - mape: 3.9424 - val_loss: 0.1609 - val_rmse: 0.3131 - val_mape: 1.1384
Epoch 541/1000
75/75 [==============================] - 0s 6ms/step - loss: 1.8039 - rmse: 1.0642 - mape: 3.8989 - val_loss: 0.1633 - val_rmse: 0.3159 - val_mape: 1.1523
Epoch 542/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.7902 - rmse: 1.0640 - mape: 3.8987 - val_loss: 0.1937 - val_rmse: 0.3475 - val_mape: 1.2634
Epoch 543/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.7420 - rmse: 1.0527 - mape: 3.8543 - val_loss: 0.1580 - val_rmse: 0.3137 - val_mape: 1.1431
Epoch 544/1000
75/75 [==============================] - 0s 6ms/step - loss: 1.7495 - rmse: 1.0442 - mape: 3.8261 - val_loss: 0.1745 - val_rmse: 0.3285 - val_mape: 1.1966
Epoch 545/1000
75/75 [==============================] - 0s 6ms/step - loss: 1.7561 - rmse: 1.0469 - mape: 3.8337 - val_loss: 0.3143 - val_rmse: 0.4388 - val_mape: 1.5791
Epoch 546/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.6686 - rmse: 1.0328 - mape: 3.7886 - val_loss: 0.3080 - val_rmse: 0.4407 - val_mape: 1.6002
Epoch 547/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.6755 - rmse: 1.0311 - mape: 3.7788 - val_loss: 0.2976 - val_rmse: 0.4246 - val_mape: 1.5283
Epoch 548/1000
75/75 [==============================] - 0s 6ms/step - loss: 1.6996 - rmse: 1.0228 - mape: 3.7465 - val_loss: 0.2175 - val_rmse: 0.3597 - val_mape: 1.2982
Epoch 549/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.8889 - rmse: 1.0888 - mape: 3.9891 - val_loss: 0.3101 - val_rmse: 0.4508 - val_mape: 1.6387
Epoch 550/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.6732 - rmse: 1.0190 - mape: 3.7417 - val_loss: 0.1966 - val_rmse: 0.3506 - val_mape: 1.2789
Epoch 551/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.7705 - rmse: 1.0517 - mape: 3.8517 - val_loss: 0.1686 - val_rmse: 0.3151 - val_mape: 1.1466
Epoch 552/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.5852 - rmse: 0.9966 - mape: 3.6550 - val_loss: 0.1914 - val_rmse: 0.3411 - val_mape: 1.2397
Epoch 553/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.7280 - rmse: 1.0400 - mape: 3.8079 - val_loss: 0.2712 - val_rmse: 0.4139 - val_mape: 1.5051
Epoch 554/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.6416 - rmse: 1.0167 - mape: 3.7254 - val_loss: 0.3226 - val_rmse: 0.4638 - val_mape: 1.6881
Epoch 555/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.7884 - rmse: 1.0555 - mape: 3.8608 - val_loss: 0.2464 - val_rmse: 0.3967 - val_mape: 1.4610
Epoch 556/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.6758 - rmse: 1.0328 - mape: 3.7855 - val_loss: 0.4396 - val_rmse: 0.5620 - val_mape: 2.0555
Epoch 557/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.6834 - rmse: 1.0288 - mape: 3.7708 - val_loss: 0.1790 - val_rmse: 0.3276 - val_mape: 1.1914
Epoch 558/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.6055 - rmse: 0.9977 - mape: 3.6554 - val_loss: 0.2223 - val_rmse: 0.3719 - val_mape: 1.3487
Epoch 559/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.6958 - rmse: 1.0326 - mape: 3.7827 - val_loss: 0.1732 - val_rmse: 0.3272 - val_mape: 1.1965
Epoch 560/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.6398 - rmse: 1.0086 - mape: 3.6946 - val_loss: 0.1656 - val_rmse: 0.3206 - val_mape: 1.1677
Epoch 561/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.6572 - rmse: 1.0167 - mape: 3.7240 - val_loss: 0.1844 - val_rmse: 0.3314 - val_mape: 1.2002
Epoch 562/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.6219 - rmse: 1.0023 - mape: 3.6725 - val_loss: 0.1999 - val_rmse: 0.3499 - val_mape: 1.2711
Epoch 563/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.7308 - rmse: 1.0452 - mape: 3.8298 - val_loss: 0.2932 - val_rmse: 0.4304 - val_mape: 1.5628
Epoch 564/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.6064 - rmse: 0.9972 - mape: 3.6535 - val_loss: 0.2017 - val_rmse: 0.3575 - val_mape: 1.3038
Epoch 565/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.5250 - rmse: 0.9827 - mape: 3.6011 - val_loss: 0.1652 - val_rmse: 0.3169 - val_mape: 1.1565
Epoch 566/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.5276 - rmse: 0.9841 - mape: 3.6071 - val_loss: 0.3718 - val_rmse: 0.4929 - val_mape: 1.7840
Epoch 567/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.6570 - rmse: 1.0223 - mape: 3.7386 - val_loss: 0.1774 - val_rmse: 0.3279 - val_mape: 1.1960
Epoch 568/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.5864 - rmse: 1.0145 - mape: 3.7146 - val_loss: 0.1931 - val_rmse: 0.3354 - val_mape: 1.2127
Epoch 569/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.6542 - rmse: 1.0228 - mape: 3.7487 - val_loss: 0.3978 - val_rmse: 0.5089 - val_mape: 1.8360
Epoch 570/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.5415 - rmse: 0.9879 - mape: 3.6243 - val_loss: 0.2585 - val_rmse: 0.4172 - val_mape: 1.5440
Epoch 571/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.5462 - rmse: 0.9857 - mape: 3.6099 - val_loss: 0.1868 - val_rmse: 0.3259 - val_mape: 1.1856
Epoch 572/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.5520 - rmse: 0.9912 - mape: 3.6332 - val_loss: 0.1710 - val_rmse: 0.3168 - val_mape: 1.1577
Epoch 573/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.5329 - rmse: 0.9714 - mape: 3.5581 - val_loss: 0.2639 - val_rmse: 0.4057 - val_mape: 1.4774
Epoch 574/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.5452 - rmse: 0.9872 - mape: 3.6194 - val_loss: 0.2458 - val_rmse: 0.3825 - val_mape: 1.3830
Epoch 575/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.6386 - rmse: 1.0048 - mape: 3.6766 - val_loss: 0.1821 - val_rmse: 0.3309 - val_mape: 1.2100
Epoch 576/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.5356 - rmse: 0.9914 - mape: 3.6330 - val_loss: 0.2233 - val_rmse: 0.3653 - val_mape: 1.3226
Epoch 577/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.5046 - rmse: 0.9811 - mape: 3.5940 - val_loss: 0.1607 - val_rmse: 0.3081 - val_mape: 1.1217
Epoch 578/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.5603 - rmse: 0.9861 - mape: 3.6070 - val_loss: 0.1649 - val_rmse: 0.3144 - val_mape: 1.1440
Epoch 579/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.4975 - rmse: 0.9786 - mape: 3.5877 - val_loss: 0.1888 - val_rmse: 0.3325 - val_mape: 1.2048
Epoch 580/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.5103 - rmse: 0.9809 - mape: 3.5950 - val_loss: 0.1993 - val_rmse: 0.3444 - val_mape: 1.2492
Epoch 581/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.4987 - rmse: 0.9683 - mape: 3.5432 - val_loss: 0.1747 - val_rmse: 0.3259 - val_mape: 1.1890
Epoch 582/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.4285 - rmse: 0.9473 - mape: 3.4699 - val_loss: 0.2118 - val_rmse: 0.3509 - val_mape: 1.2678
Epoch 583/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.4762 - rmse: 0.9733 - mape: 3.5681 - val_loss: 0.1848 - val_rmse: 0.3367 - val_mape: 1.2269
Epoch 584/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.4953 - rmse: 0.9720 - mape: 3.5609 - val_loss: 0.1808 - val_rmse: 0.3291 - val_mape: 1.1990
Epoch 585/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.4562 - rmse: 0.9617 - mape: 3.5169 - val_loss: 0.3009 - val_rmse: 0.4547 - val_mape: 1.6834
Epoch 586/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.4767 - rmse: 0.9475 - mape: 3.4691 - val_loss: 0.2512 - val_rmse: 0.3891 - val_mape: 1.4100
Epoch 587/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.4425 - rmse: 0.9469 - mape: 3.4680 - val_loss: 0.1979 - val_rmse: 0.3423 - val_mape: 1.2412
Epoch 588/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.4372 - rmse: 0.9478 - mape: 3.4725 - val_loss: 0.1679 - val_rmse: 0.3171 - val_mape: 1.1511
Epoch 589/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.4189 - rmse: 0.9415 - mape: 3.4478 - val_loss: 0.2283 - val_rmse: 0.3686 - val_mape: 1.3319
Epoch 590/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.3414 - rmse: 0.9198 - mape: 3.3678 - val_loss: 0.1739 - val_rmse: 0.3248 - val_mape: 1.1873
Epoch 591/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.4250 - rmse: 0.9413 - mape: 3.4454 - val_loss: 0.1629 - val_rmse: 0.3154 - val_mape: 1.1528
Epoch 592/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.4770 - rmse: 0.9626 - mape: 3.5258 - val_loss: 0.2211 - val_rmse: 0.3667 - val_mape: 1.3312
Epoch 593/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.3723 - rmse: 0.9242 - mape: 3.3852 - val_loss: 0.1776 - val_rmse: 0.3209 - val_mape: 1.1617
Epoch 594/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.4414 - rmse: 0.9475 - mape: 3.4707 - val_loss: 0.1705 - val_rmse: 0.3234 - val_mape: 1.1790
Epoch 595/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.4709 - rmse: 0.9628 - mape: 3.5290 - val_loss: 0.4216 - val_rmse: 0.5409 - val_mape: 1.9605
Epoch 596/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.4272 - rmse: 0.9481 - mape: 3.4748 - val_loss: 0.1798 - val_rmse: 0.3289 - val_mape: 1.1958
Epoch 597/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.3555 - rmse: 0.9295 - mape: 3.4083 - val_loss: 0.1922 - val_rmse: 0.3385 - val_mape: 1.2253
Epoch 598/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.3642 - rmse: 0.9149 - mape: 3.3472 - val_loss: 0.1668 - val_rmse: 0.3219 - val_mape: 1.1760
Epoch 599/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.3108 - rmse: 0.9043 - mape: 3.3118 - val_loss: 0.1719 - val_rmse: 0.3192 - val_mape: 1.1631
Epoch 600/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.4047 - rmse: 0.9367 - mape: 3.4280 - val_loss: 0.1712 - val_rmse: 0.3258 - val_mape: 1.1887
Epoch 601/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.3516 - rmse: 0.9162 - mape: 3.3546 - val_loss: 0.1752 - val_rmse: 0.3286 - val_mape: 1.1980
Epoch 602/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.3233 - rmse: 0.9140 - mape: 3.3456 - val_loss: 0.1994 - val_rmse: 0.3486 - val_mape: 1.2687
Epoch 603/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.3280 - rmse: 0.9044 - mape: 3.3128 - val_loss: 0.1864 - val_rmse: 0.3299 - val_mape: 1.1953
Epoch 604/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2362 - rmse: 0.8802 - mape: 3.2231 - val_loss: 0.1990 - val_rmse: 0.3497 - val_mape: 1.2722
Epoch 605/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2993 - rmse: 0.8966 - mape: 3.2785 - val_loss: 0.2536 - val_rmse: 0.3969 - val_mape: 1.4448
Epoch 606/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2627 - rmse: 0.8960 - mape: 3.2789 - val_loss: 0.1771 - val_rmse: 0.3252 - val_mape: 1.1800
Epoch 607/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2658 - rmse: 0.8931 - mape: 3.2694 - val_loss: 0.1918 - val_rmse: 0.3414 - val_mape: 1.2431
Epoch 608/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.3004 - rmse: 0.8956 - mape: 3.2753 - val_loss: 0.2341 - val_rmse: 0.3802 - val_mape: 1.3797
Epoch 609/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2720 - rmse: 0.8897 - mape: 3.2552 - val_loss: 0.2098 - val_rmse: 0.3542 - val_mape: 1.2809
Epoch 610/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.3038 - rmse: 0.9060 - mape: 3.3158 - val_loss: 0.2060 - val_rmse: 0.3543 - val_mape: 1.2844
Epoch 611/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2859 - rmse: 0.8895 - mape: 3.2572 - val_loss: 0.1898 - val_rmse: 0.3333 - val_mape: 1.2094
Epoch 612/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2294 - rmse: 0.8775 - mape: 3.2120 - val_loss: 0.1992 - val_rmse: 0.3490 - val_mape: 1.2706
Epoch 613/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2926 - rmse: 0.8909 - mape: 3.2578 - val_loss: 0.1698 - val_rmse: 0.3237 - val_mape: 1.1819
Epoch 614/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.3397 - rmse: 0.9106 - mape: 3.3345 - val_loss: 0.3048 - val_rmse: 0.4440 - val_mape: 1.6148
Epoch 615/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2359 - rmse: 0.8846 - mape: 3.2384 - val_loss: 0.1748 - val_rmse: 0.3274 - val_mape: 1.1862
Epoch 616/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2136 - rmse: 0.8733 - mape: 3.1914 - val_loss: 0.1951 - val_rmse: 0.3461 - val_mape: 1.2597
Epoch 617/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2860 - rmse: 0.8961 - mape: 3.2766 - val_loss: 0.1672 - val_rmse: 0.3170 - val_mape: 1.1530
Epoch 618/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2375 - rmse: 0.8821 - mape: 3.2306 - val_loss: 0.1780 - val_rmse: 0.3329 - val_mape: 1.2188
Epoch 619/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2362 - rmse: 0.8734 - mape: 3.1962 - val_loss: 0.1516 - val_rmse: 0.3046 - val_mape: 1.1137
Epoch 620/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2342 - rmse: 0.8717 - mape: 3.1918 - val_loss: 0.2615 - val_rmse: 0.4002 - val_mape: 1.4472
Epoch 621/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.1644 - rmse: 0.8584 - mape: 3.1402 - val_loss: 0.1744 - val_rmse: 0.3227 - val_mape: 1.1727
Epoch 622/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2132 - rmse: 0.8770 - mape: 3.2111 - val_loss: 0.1685 - val_rmse: 0.3200 - val_mape: 1.1663
Epoch 623/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2171 - rmse: 0.8771 - mape: 3.2077 - val_loss: 0.1692 - val_rmse: 0.3213 - val_mape: 1.1684
Epoch 624/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2358 - rmse: 0.8857 - mape: 3.2404 - val_loss: 0.1571 - val_rmse: 0.3052 - val_mape: 1.1096
Epoch 625/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.1606 - rmse: 0.8497 - mape: 3.1105 - val_loss: 0.2876 - val_rmse: 0.4290 - val_mape: 1.5569
Epoch 626/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2099 - rmse: 0.8761 - mape: 3.2054 - val_loss: 0.1576 - val_rmse: 0.3061 - val_mape: 1.1151
Epoch 627/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.1307 - rmse: 0.8430 - mape: 3.0860 - val_loss: 0.3258 - val_rmse: 0.4551 - val_mape: 1.6469
Epoch 628/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.1037 - rmse: 0.8352 - mape: 3.0540 - val_loss: 0.3061 - val_rmse: 0.4339 - val_mape: 1.5644
Epoch 629/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2438 - rmse: 0.8818 - mape: 3.2283 - val_loss: 0.1586 - val_rmse: 0.3084 - val_mape: 1.1250
Epoch 630/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.1879 - rmse: 0.8588 - mape: 3.1420 - val_loss: 0.1919 - val_rmse: 0.3414 - val_mape: 1.2501
Epoch 631/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.2571 - rmse: 0.8895 - mape: 3.2544 - val_loss: 0.2350 - val_rmse: 0.3794 - val_mape: 1.3711
Epoch 632/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.1272 - rmse: 0.8268 - mape: 3.0204 - val_loss: 0.1786 - val_rmse: 0.3298 - val_mape: 1.1965
Epoch 633/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.1394 - rmse: 0.8458 - mape: 3.0945 - val_loss: 0.2061 - val_rmse: 0.3621 - val_mape: 1.3310
Epoch 634/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.1500 - rmse: 0.8464 - mape: 3.0995 - val_loss: 0.1778 - val_rmse: 0.3239 - val_mape: 1.1745
Epoch 635/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.0985 - rmse: 0.8270 - mape: 3.0230 - val_loss: 0.3067 - val_rmse: 0.4333 - val_mape: 1.5569
Epoch 636/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.1942 - rmse: 0.8618 - mape: 3.1479 - val_loss: 0.1696 - val_rmse: 0.3245 - val_mape: 1.1853
Epoch 637/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.1423 - rmse: 0.8423 - mape: 3.0798 - val_loss: 0.2238 - val_rmse: 0.3753 - val_mape: 1.3702
Epoch 638/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.0627 - rmse: 0.8169 - mape: 2.9884 - val_loss: 0.1817 - val_rmse: 0.3320 - val_mape: 1.2064
Epoch 639/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.1472 - rmse: 0.8491 - mape: 3.1032 - val_loss: 0.1948 - val_rmse: 0.3402 - val_mape: 1.2311
Epoch 640/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.1112 - rmse: 0.8409 - mape: 3.0782 - val_loss: 0.1498 - val_rmse: 0.2994 - val_mape: 1.0910
Epoch 641/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.0785 - rmse: 0.8172 - mape: 2.9932 - val_loss: 0.1688 - val_rmse: 0.3185 - val_mape: 1.1575
Epoch 642/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.0784 - rmse: 0.8258 - mape: 3.0215 - val_loss: 0.1487 - val_rmse: 0.2993 - val_mape: 1.0886
Epoch 643/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.0638 - rmse: 0.8154 - mape: 2.9825 - val_loss: 0.1776 - val_rmse: 0.3286 - val_mape: 1.1931
Epoch 644/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.1122 - rmse: 0.8331 - mape: 3.0484 - val_loss: 0.2419 - val_rmse: 0.3922 - val_mape: 1.4389
Epoch 645/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.0331 - rmse: 0.8086 - mape: 2.9591 - val_loss: 0.4566 - val_rmse: 0.5719 - val_mape: 2.0802
Epoch 646/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.0860 - rmse: 0.8235 - mape: 3.0118 - val_loss: 0.1756 - val_rmse: 0.3365 - val_mape: 1.2371
Epoch 647/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.9996 - rmse: 0.7978 - mape: 2.9212 - val_loss: 0.1904 - val_rmse: 0.3401 - val_mape: 1.2365
Epoch 648/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.0175 - rmse: 0.7917 - mape: 2.8953 - val_loss: 0.1989 - val_rmse: 0.3479 - val_mape: 1.2594
Epoch 649/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.0273 - rmse: 0.7965 - mape: 2.9120 - val_loss: 0.1947 - val_rmse: 0.3434 - val_mape: 1.2425
Epoch 650/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.0537 - rmse: 0.8109 - mape: 2.9631 - val_loss: 0.1551 - val_rmse: 0.3106 - val_mape: 1.1330
Epoch 651/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.0340 - rmse: 0.8007 - mape: 2.9256 - val_loss: 0.1626 - val_rmse: 0.3139 - val_mape: 1.1459
Epoch 652/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.0148 - rmse: 0.7958 - mape: 2.9062 - val_loss: 0.1626 - val_rmse: 0.3168 - val_mape: 1.1522
Epoch 653/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.0086 - rmse: 0.7927 - mape: 2.8980 - val_loss: 0.2463 - val_rmse: 0.3902 - val_mape: 1.4135
Epoch 654/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.0381 - rmse: 0.7967 - mape: 2.9093 - val_loss: 0.3022 - val_rmse: 0.4382 - val_mape: 1.5825
Epoch 655/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.0331 - rmse: 0.8040 - mape: 2.9419 - val_loss: 0.1582 - val_rmse: 0.3141 - val_mape: 1.1469
Epoch 656/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.9744 - rmse: 0.7749 - mape: 2.8324 - val_loss: 0.1651 - val_rmse: 0.3174 - val_mape: 1.1562
Epoch 657/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.9528 - rmse: 0.7764 - mape: 2.8425 - val_loss: 0.1526 - val_rmse: 0.3042 - val_mape: 1.1065
Epoch 658/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.9335 - rmse: 0.7593 - mape: 2.7739 - val_loss: 0.2062 - val_rmse: 0.3438 - val_mape: 1.2388
Epoch 659/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.9696 - rmse: 0.7770 - mape: 2.8384 - val_loss: 0.2343 - val_rmse: 0.3849 - val_mape: 1.3974
Epoch 660/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.9383 - rmse: 0.7728 - mape: 2.8259 - val_loss: 0.1692 - val_rmse: 0.3222 - val_mape: 1.1786
Epoch 661/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.9649 - rmse: 0.7758 - mape: 2.8368 - val_loss: 0.1557 - val_rmse: 0.3063 - val_mape: 1.1134
Epoch 662/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.9298 - rmse: 0.7645 - mape: 2.7963 - val_loss: 0.4825 - val_rmse: 0.5882 - val_mape: 2.1302
Epoch 663/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.9365 - rmse: 0.7693 - mape: 2.8132 - val_loss: 0.1545 - val_rmse: 0.3079 - val_mape: 1.1233
Epoch 664/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.9739 - rmse: 0.7733 - mape: 2.8247 - val_loss: 0.1721 - val_rmse: 0.3235 - val_mape: 1.1742
Epoch 665/1000
75/75 [==============================] - 0s 5ms/step - loss: 1.0534 - rmse: 0.8027 - mape: 2.9292 - val_loss: 0.1972 - val_rmse: 0.3428 - val_mape: 1.2411
Epoch 666/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.9234 - rmse: 0.7611 - mape: 2.7830 - val_loss: 0.1934 - val_rmse: 0.3396 - val_mape: 1.2323
Epoch 667/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.9518 - rmse: 0.7741 - mape: 2.8298 - val_loss: 0.3139 - val_rmse: 0.4464 - val_mape: 1.6116
Epoch 668/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.9177 - rmse: 0.7563 - mape: 2.7650 - val_loss: 0.1768 - val_rmse: 0.3262 - val_mape: 1.1909
Epoch 669/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.9211 - rmse: 0.7582 - mape: 2.7741 - val_loss: 0.1721 - val_rmse: 0.3225 - val_mape: 1.1708
Epoch 670/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.8793 - rmse: 0.7328 - mape: 2.6793 - val_loss: 0.1557 - val_rmse: 0.3088 - val_mape: 1.1262
Epoch 671/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.9269 - rmse: 0.7564 - mape: 2.7618 - val_loss: 0.1573 - val_rmse: 0.3104 - val_mape: 1.1304
Epoch 672/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.9475 - rmse: 0.7650 - mape: 2.7935 - val_loss: 0.2055 - val_rmse: 0.3522 - val_mape: 1.2718
Epoch 673/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.8424 - rmse: 0.7267 - mape: 2.6554 - val_loss: 0.1506 - val_rmse: 0.3030 - val_mape: 1.1031
Epoch 674/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.9011 - rmse: 0.7509 - mape: 2.7455 - val_loss: 0.1706 - val_rmse: 0.3255 - val_mape: 1.1907
Epoch 675/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.8902 - rmse: 0.7370 - mape: 2.6925 - val_loss: 0.2445 - val_rmse: 0.3728 - val_mape: 1.3403
Epoch 676/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.8461 - rmse: 0.7273 - mape: 2.6591 - val_loss: 0.2464 - val_rmse: 0.3971 - val_mape: 1.4440
Epoch 677/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.8590 - rmse: 0.7350 - mape: 2.6890 - val_loss: 0.2195 - val_rmse: 0.3579 - val_mape: 1.2930
Epoch 678/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.8959 - rmse: 0.7399 - mape: 2.7000 - val_loss: 0.1715 - val_rmse: 0.3207 - val_mape: 1.1633
Epoch 679/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.8788 - rmse: 0.7391 - mape: 2.6985 - val_loss: 0.1997 - val_rmse: 0.3419 - val_mape: 1.2364
Epoch 680/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.8579 - rmse: 0.7248 - mape: 2.6474 - val_loss: 0.1847 - val_rmse: 0.3299 - val_mape: 1.1957
Epoch 681/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.8416 - rmse: 0.7258 - mape: 2.6557 - val_loss: 0.1901 - val_rmse: 0.3383 - val_mape: 1.2265
Epoch 682/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.8399 - rmse: 0.7222 - mape: 2.6408 - val_loss: 0.1568 - val_rmse: 0.3133 - val_mape: 1.1423
Epoch 683/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.8418 - rmse: 0.7177 - mape: 2.6221 - val_loss: 0.2422 - val_rmse: 0.3892 - val_mape: 1.4162
Epoch 684/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.8621 - rmse: 0.7293 - mape: 2.6637 - val_loss: 0.1522 - val_rmse: 0.3000 - val_mape: 1.0898
Epoch 685/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.9075 - rmse: 0.7414 - mape: 2.7066 - val_loss: 0.2620 - val_rmse: 0.4013 - val_mape: 1.4513
Epoch 686/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.8019 - rmse: 0.7057 - mape: 2.5766 - val_loss: 0.1641 - val_rmse: 0.3145 - val_mape: 1.1429
Epoch 687/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7883 - rmse: 0.6967 - mape: 2.5479 - val_loss: 0.1680 - val_rmse: 0.3202 - val_mape: 1.1711
Epoch 688/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.8394 - rmse: 0.7187 - mape: 2.6231 - val_loss: 0.1510 - val_rmse: 0.3057 - val_mape: 1.1154
Epoch 689/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7774 - rmse: 0.6950 - mape: 2.5362 - val_loss: 0.2610 - val_rmse: 0.4091 - val_mape: 1.4916
Epoch 690/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.8418 - rmse: 0.7224 - mape: 2.6382 - val_loss: 0.1863 - val_rmse: 0.3271 - val_mape: 1.1819
Epoch 691/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7875 - rmse: 0.6985 - mape: 2.5532 - val_loss: 0.1710 - val_rmse: 0.3207 - val_mape: 1.1705
Epoch 692/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7483 - rmse: 0.6815 - mape: 2.4915 - val_loss: 0.1937 - val_rmse: 0.3365 - val_mape: 1.2163
Epoch 693/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.8089 - rmse: 0.6971 - mape: 2.5444 - val_loss: 0.1513 - val_rmse: 0.2982 - val_mape: 1.0849
Epoch 694/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7479 - rmse: 0.6787 - mape: 2.4769 - val_loss: 0.1620 - val_rmse: 0.3057 - val_mape: 1.1104
Epoch 695/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7401 - rmse: 0.6741 - mape: 2.4649 - val_loss: 0.1832 - val_rmse: 0.3332 - val_mape: 1.2070
Epoch 696/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7833 - rmse: 0.6939 - mape: 2.5327 - val_loss: 0.2870 - val_rmse: 0.4287 - val_mape: 1.5531
Epoch 697/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7483 - rmse: 0.6761 - mape: 2.4691 - val_loss: 0.2423 - val_rmse: 0.3869 - val_mape: 1.3976
Epoch 698/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7261 - rmse: 0.6650 - mape: 2.4281 - val_loss: 0.1722 - val_rmse: 0.3210 - val_mape: 1.1623
Epoch 699/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7970 - rmse: 0.7025 - mape: 2.5650 - val_loss: 0.2011 - val_rmse: 0.3474 - val_mape: 1.2576
Epoch 700/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7615 - rmse: 0.6816 - mape: 2.4881 - val_loss: 0.1727 - val_rmse: 0.3278 - val_mape: 1.2035
Epoch 701/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7436 - rmse: 0.6797 - mape: 2.4853 - val_loss: 0.1786 - val_rmse: 0.3227 - val_mape: 1.1691
Epoch 702/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7670 - rmse: 0.6923 - mape: 2.5248 - val_loss: 0.1901 - val_rmse: 0.3316 - val_mape: 1.1982
Epoch 703/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7642 - rmse: 0.6856 - mape: 2.5035 - val_loss: 0.2890 - val_rmse: 0.4194 - val_mape: 1.5093
Epoch 704/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7310 - rmse: 0.6710 - mape: 2.4519 - val_loss: 0.1626 - val_rmse: 0.3130 - val_mape: 1.1348
Epoch 705/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6936 - rmse: 0.6568 - mape: 2.4012 - val_loss: 0.2524 - val_rmse: 0.3930 - val_mape: 1.4174
Epoch 706/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7165 - rmse: 0.6684 - mape: 2.4432 - val_loss: 0.1636 - val_rmse: 0.3163 - val_mape: 1.1512
Epoch 707/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7346 - rmse: 0.6743 - mape: 2.4641 - val_loss: 0.1968 - val_rmse: 0.3418 - val_mape: 1.2392
Epoch 708/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7337 - rmse: 0.6715 - mape: 2.4506 - val_loss: 0.1835 - val_rmse: 0.3238 - val_mape: 1.1735
Epoch 709/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7251 - rmse: 0.6725 - mape: 2.4546 - val_loss: 0.2139 - val_rmse: 0.3546 - val_mape: 1.2805
Epoch 710/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7411 - rmse: 0.6791 - mape: 2.4773 - val_loss: 0.1796 - val_rmse: 0.3284 - val_mape: 1.1942
Epoch 711/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7222 - rmse: 0.6722 - mape: 2.4549 - val_loss: 0.2119 - val_rmse: 0.3618 - val_mape: 1.3133
Epoch 712/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6879 - rmse: 0.6496 - mape: 2.3752 - val_loss: 0.1597 - val_rmse: 0.3109 - val_mape: 1.1304
Epoch 713/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7098 - rmse: 0.6585 - mape: 2.4029 - val_loss: 0.3262 - val_rmse: 0.4603 - val_mape: 1.6658
Epoch 714/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6824 - rmse: 0.6501 - mape: 2.3719 - val_loss: 0.1993 - val_rmse: 0.3450 - val_mape: 1.2469
Epoch 715/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6570 - rmse: 0.6360 - mape: 2.3225 - val_loss: 0.1580 - val_rmse: 0.3137 - val_mape: 1.1467
Epoch 716/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.7032 - rmse: 0.6548 - mape: 2.3886 - val_loss: 0.1670 - val_rmse: 0.3203 - val_mape: 1.1655
Epoch 717/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6774 - rmse: 0.6473 - mape: 2.3628 - val_loss: 0.1614 - val_rmse: 0.3143 - val_mape: 1.1487
Epoch 718/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6564 - rmse: 0.6345 - mape: 2.3153 - val_loss: 0.2473 - val_rmse: 0.3786 - val_mape: 1.3604
Epoch 719/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6533 - rmse: 0.6339 - mape: 2.3076 - val_loss: 0.1733 - val_rmse: 0.3204 - val_mape: 1.1635
Epoch 720/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6929 - rmse: 0.6509 - mape: 2.3763 - val_loss: 0.1812 - val_rmse: 0.3339 - val_mape: 1.2236
Epoch 721/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6515 - rmse: 0.6320 - mape: 2.3085 - val_loss: 0.2318 - val_rmse: 0.3818 - val_mape: 1.3954
Epoch 722/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6428 - rmse: 0.6317 - mape: 2.3069 - val_loss: 0.1591 - val_rmse: 0.3108 - val_mape: 1.1320
Epoch 723/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6637 - rmse: 0.6374 - mape: 2.3259 - val_loss: 0.2671 - val_rmse: 0.4008 - val_mape: 1.4477
Epoch 724/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6204 - rmse: 0.6133 - mape: 2.2375 - val_loss: 0.1976 - val_rmse: 0.3422 - val_mape: 1.2408
Epoch 725/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6169 - rmse: 0.6180 - mape: 2.2543 - val_loss: 0.1578 - val_rmse: 0.3102 - val_mape: 1.1295
Epoch 726/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6339 - rmse: 0.6221 - mape: 2.2677 - val_loss: 0.1617 - val_rmse: 0.3138 - val_mape: 1.1445
Epoch 727/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6262 - rmse: 0.6118 - mape: 2.2292 - val_loss: 0.1764 - val_rmse: 0.3305 - val_mape: 1.2061
Epoch 728/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6249 - rmse: 0.6100 - mape: 2.2225 - val_loss: 0.1671 - val_rmse: 0.3168 - val_mape: 1.1531
Epoch 729/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5979 - rmse: 0.6125 - mape: 2.2354 - val_loss: 0.1847 - val_rmse: 0.3330 - val_mape: 1.2071
Epoch 730/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6239 - rmse: 0.6198 - mape: 2.2631 - val_loss: 0.1648 - val_rmse: 0.3086 - val_mape: 1.1250
Epoch 731/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5851 - rmse: 0.6008 - mape: 2.1937 - val_loss: 0.2626 - val_rmse: 0.4042 - val_mape: 1.4653
Epoch 732/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5713 - rmse: 0.5960 - mape: 2.1794 - val_loss: 0.2129 - val_rmse: 0.3613 - val_mape: 1.3135
Epoch 733/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5852 - rmse: 0.6045 - mape: 2.2052 - val_loss: 0.2490 - val_rmse: 0.3842 - val_mape: 1.3873
Epoch 734/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5905 - rmse: 0.6052 - mape: 2.2058 - val_loss: 0.2633 - val_rmse: 0.4080 - val_mape: 1.4816
Epoch 735/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5716 - rmse: 0.5902 - mape: 2.1520 - val_loss: 0.2064 - val_rmse: 0.3479 - val_mape: 1.2578
Epoch 736/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6417 - rmse: 0.6238 - mape: 2.2717 - val_loss: 0.1801 - val_rmse: 0.3232 - val_mape: 1.1705
Epoch 737/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6062 - rmse: 0.6087 - mape: 2.2175 - val_loss: 0.2000 - val_rmse: 0.3420 - val_mape: 1.2339
Epoch 738/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.6002 - rmse: 0.6072 - mape: 2.2117 - val_loss: 0.1555 - val_rmse: 0.3072 - val_mape: 1.1172
Epoch 739/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5800 - rmse: 0.5937 - mape: 2.1648 - val_loss: 0.1512 - val_rmse: 0.2997 - val_mape: 1.0908
Epoch 740/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5806 - rmse: 0.5963 - mape: 2.1739 - val_loss: 0.1813 - val_rmse: 0.3322 - val_mape: 1.2087
Epoch 741/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5813 - rmse: 0.5955 - mape: 2.1711 - val_loss: 0.2019 - val_rmse: 0.3468 - val_mape: 1.2534
Epoch 742/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5366 - rmse: 0.5754 - mape: 2.0986 - val_loss: 0.1596 - val_rmse: 0.3128 - val_mape: 1.1411
Epoch 743/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5360 - rmse: 0.5780 - mape: 2.1069 - val_loss: 0.1870 - val_rmse: 0.3380 - val_mape: 1.2275
Epoch 744/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5754 - rmse: 0.5935 - mape: 2.1596 - val_loss: 0.1724 - val_rmse: 0.3267 - val_mape: 1.1961
Epoch 745/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5392 - rmse: 0.5741 - mape: 2.0893 - val_loss: 0.1827 - val_rmse: 0.3283 - val_mape: 1.1914
Epoch 746/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5623 - rmse: 0.5822 - mape: 2.1231 - val_loss: 0.1666 - val_rmse: 0.3174 - val_mape: 1.1573
Epoch 747/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5375 - rmse: 0.5762 - mape: 2.0981 - val_loss: 0.1838 - val_rmse: 0.3314 - val_mape: 1.2023
Epoch 748/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5226 - rmse: 0.5621 - mape: 2.0494 - val_loss: 0.1541 - val_rmse: 0.3032 - val_mape: 1.1055
Epoch 749/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5311 - rmse: 0.5691 - mape: 2.0747 - val_loss: 0.1769 - val_rmse: 0.3340 - val_mape: 1.2245
Epoch 750/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5227 - rmse: 0.5615 - mape: 2.0461 - val_loss: 0.1856 - val_rmse: 0.3300 - val_mape: 1.1988
Epoch 751/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5527 - rmse: 0.5782 - mape: 2.1075 - val_loss: 0.1804 - val_rmse: 0.3257 - val_mape: 1.1793
Epoch 752/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4969 - rmse: 0.5467 - mape: 1.9911 - val_loss: 0.1557 - val_rmse: 0.3067 - val_mape: 1.1163
Epoch 753/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5209 - rmse: 0.5639 - mape: 2.0538 - val_loss: 0.1964 - val_rmse: 0.3489 - val_mape: 1.2666
Epoch 754/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5204 - rmse: 0.5611 - mape: 2.0413 - val_loss: 0.2025 - val_rmse: 0.3466 - val_mape: 1.2520
Epoch 755/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5032 - rmse: 0.5492 - mape: 1.9999 - val_loss: 0.1917 - val_rmse: 0.3422 - val_mape: 1.2411
Epoch 756/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5173 - rmse: 0.5588 - mape: 2.0345 - val_loss: 0.1656 - val_rmse: 0.3153 - val_mape: 1.1464
Epoch 757/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5130 - rmse: 0.5575 - mape: 2.0309 - val_loss: 0.1936 - val_rmse: 0.3475 - val_mape: 1.2678
Epoch 758/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.5109 - rmse: 0.5627 - mape: 2.0479 - val_loss: 0.2018 - val_rmse: 0.3587 - val_mape: 1.3111
Epoch 759/1000
75/75 [==============================] - 0s 6ms/step - loss: 0.4872 - rmse: 0.5434 - mape: 1.9787 - val_loss: 0.2121 - val_rmse: 0.3574 - val_mape: 1.2935
Epoch 760/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4872 - rmse: 0.5441 - mape: 1.9837 - val_loss: 0.1787 - val_rmse: 0.3292 - val_mape: 1.1941
Epoch 761/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4640 - rmse: 0.5312 - mape: 1.9338 - val_loss: 0.1818 - val_rmse: 0.3320 - val_mape: 1.2057
Epoch 762/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4760 - rmse: 0.5350 - mape: 1.9473 - val_loss: 0.1638 - val_rmse: 0.3094 - val_mape: 1.1219
Epoch 763/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4383 - rmse: 0.5184 - mape: 1.8894 - val_loss: 0.1672 - val_rmse: 0.3157 - val_mape: 1.1485
Epoch 764/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4697 - rmse: 0.5346 - mape: 1.9474 - val_loss: 0.2656 - val_rmse: 0.4048 - val_mape: 1.4638
Epoch 765/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4595 - rmse: 0.5289 - mape: 1.9253 - val_loss: 0.2010 - val_rmse: 0.3387 - val_mape: 1.2231
Epoch 766/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4392 - rmse: 0.5196 - mape: 1.8936 - val_loss: 0.1579 - val_rmse: 0.3039 - val_mape: 1.1028
Epoch 767/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4659 - rmse: 0.5308 - mape: 1.9329 - val_loss: 0.2296 - val_rmse: 0.3932 - val_mape: 1.4509
Epoch 768/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4319 - rmse: 0.5146 - mape: 1.8743 - val_loss: 0.1637 - val_rmse: 0.3073 - val_mape: 1.1136
Epoch 769/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4483 - rmse: 0.5177 - mape: 1.8833 - val_loss: 0.1589 - val_rmse: 0.3131 - val_mape: 1.1456
Epoch 770/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4573 - rmse: 0.5246 - mape: 1.9091 - val_loss: 0.1785 - val_rmse: 0.3271 - val_mape: 1.1853
Epoch 771/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4727 - rmse: 0.5339 - mape: 1.9436 - val_loss: 0.1656 - val_rmse: 0.3169 - val_mape: 1.1513
Epoch 772/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4500 - rmse: 0.5210 - mape: 1.8960 - val_loss: 0.1847 - val_rmse: 0.3338 - val_mape: 1.2129
Epoch 773/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4498 - rmse: 0.5186 - mape: 1.8875 - val_loss: 0.1539 - val_rmse: 0.3050 - val_mape: 1.1105
Epoch 774/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4324 - rmse: 0.5041 - mape: 1.8333 - val_loss: 0.1614 - val_rmse: 0.3125 - val_mape: 1.1409
Epoch 775/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4426 - rmse: 0.5142 - mape: 1.8703 - val_loss: 0.1684 - val_rmse: 0.3174 - val_mape: 1.1532
Epoch 776/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4469 - rmse: 0.5247 - mape: 1.9090 - val_loss: 0.1538 - val_rmse: 0.3031 - val_mape: 1.1029
Epoch 777/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4343 - rmse: 0.5101 - mape: 1.8561 - val_loss: 0.1582 - val_rmse: 0.3019 - val_mape: 1.0962
Epoch 778/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4146 - rmse: 0.4962 - mape: 1.8040 - val_loss: 0.1958 - val_rmse: 0.3396 - val_mape: 1.2313
Epoch 779/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4139 - rmse: 0.4979 - mape: 1.8120 - val_loss: 0.1962 - val_rmse: 0.3436 - val_mape: 1.2492
Epoch 780/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4106 - rmse: 0.4990 - mape: 1.8161 - val_loss: 0.1723 - val_rmse: 0.3325 - val_mape: 1.2215
Epoch 781/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3969 - rmse: 0.4918 - mape: 1.7918 - val_loss: 0.1550 - val_rmse: 0.3020 - val_mape: 1.0971
Epoch 782/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4011 - rmse: 0.4870 - mape: 1.7719 - val_loss: 0.1595 - val_rmse: 0.3080 - val_mape: 1.1221
Epoch 783/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4040 - rmse: 0.4889 - mape: 1.7769 - val_loss: 0.1604 - val_rmse: 0.3111 - val_mape: 1.1324
Epoch 784/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3796 - rmse: 0.4813 - mape: 1.7530 - val_loss: 0.1711 - val_rmse: 0.3193 - val_mape: 1.1575
Epoch 785/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.4169 - rmse: 0.4999 - mape: 1.8183 - val_loss: 0.1663 - val_rmse: 0.3179 - val_mape: 1.1559
Epoch 786/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3784 - rmse: 0.4782 - mape: 1.7406 - val_loss: 0.1594 - val_rmse: 0.3087 - val_mape: 1.1218
Epoch 787/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3923 - rmse: 0.4870 - mape: 1.7722 - val_loss: 0.1716 - val_rmse: 0.3237 - val_mape: 1.1764
Epoch 788/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3801 - rmse: 0.4766 - mape: 1.7347 - val_loss: 0.1949 - val_rmse: 0.3477 - val_mape: 1.2649
Epoch 789/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3799 - rmse: 0.4766 - mape: 1.7327 - val_loss: 0.1828 - val_rmse: 0.3310 - val_mape: 1.1993
Epoch 790/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3712 - rmse: 0.4693 - mape: 1.7072 - val_loss: 0.1554 - val_rmse: 0.3060 - val_mape: 1.1166
Epoch 791/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3637 - rmse: 0.4689 - mape: 1.7046 - val_loss: 0.1612 - val_rmse: 0.3140 - val_mape: 1.1439
Epoch 792/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3884 - rmse: 0.4781 - mape: 1.7370 - val_loss: 0.1646 - val_rmse: 0.3153 - val_mape: 1.1472
Epoch 793/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3909 - rmse: 0.4877 - mape: 1.7710 - val_loss: 0.1684 - val_rmse: 0.3192 - val_mape: 1.1649
Epoch 794/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3672 - rmse: 0.4696 - mape: 1.7086 - val_loss: 0.2385 - val_rmse: 0.3866 - val_mape: 1.4027
Epoch 795/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3775 - rmse: 0.4764 - mape: 1.7317 - val_loss: 0.1777 - val_rmse: 0.3265 - val_mape: 1.1828
Epoch 796/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3438 - rmse: 0.4502 - mape: 1.6341 - val_loss: 0.1720 - val_rmse: 0.3239 - val_mape: 1.1766
Epoch 797/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3483 - rmse: 0.4585 - mape: 1.6678 - val_loss: 0.2486 - val_rmse: 0.3943 - val_mape: 1.4317
Epoch 798/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3516 - rmse: 0.4559 - mape: 1.6578 - val_loss: 0.1739 - val_rmse: 0.3222 - val_mape: 1.1725
Epoch 799/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3371 - rmse: 0.4446 - mape: 1.6132 - val_loss: 0.2046 - val_rmse: 0.3480 - val_mape: 1.2593
Epoch 800/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3468 - rmse: 0.4538 - mape: 1.6483 - val_loss: 0.1697 - val_rmse: 0.3188 - val_mape: 1.1557
Epoch 801/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3247 - rmse: 0.4426 - mape: 1.6082 - val_loss: 0.1645 - val_rmse: 0.3162 - val_mape: 1.1506
Epoch 802/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3227 - rmse: 0.4371 - mape: 1.5866 - val_loss: 0.1821 - val_rmse: 0.3324 - val_mape: 1.2072
Epoch 803/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3332 - rmse: 0.4420 - mape: 1.6056 - val_loss: 0.1495 - val_rmse: 0.3008 - val_mape: 1.0966
Epoch 804/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3319 - rmse: 0.4424 - mape: 1.6068 - val_loss: 0.2077 - val_rmse: 0.3485 - val_mape: 1.2588
Epoch 805/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3257 - rmse: 0.4403 - mape: 1.5993 - val_loss: 0.1793 - val_rmse: 0.3245 - val_mape: 1.1754
Epoch 806/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3191 - rmse: 0.4359 - mape: 1.5820 - val_loss: 0.1600 - val_rmse: 0.3104 - val_mape: 1.1285
Epoch 807/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3354 - rmse: 0.4465 - mape: 1.6211 - val_loss: 0.1478 - val_rmse: 0.2981 - val_mape: 1.0862
Epoch 808/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3124 - rmse: 0.4322 - mape: 1.5685 - val_loss: 0.1792 - val_rmse: 0.3332 - val_mape: 1.2177
Epoch 809/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3098 - rmse: 0.4325 - mape: 1.5730 - val_loss: 0.1592 - val_rmse: 0.3121 - val_mape: 1.1359
Epoch 810/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3206 - rmse: 0.4372 - mape: 1.5867 - val_loss: 0.1795 - val_rmse: 0.3282 - val_mape: 1.1876
Epoch 811/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3210 - rmse: 0.4355 - mape: 1.5821 - val_loss: 0.1796 - val_rmse: 0.3285 - val_mape: 1.1891
Epoch 812/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3125 - rmse: 0.4292 - mape: 1.5588 - val_loss: 0.2124 - val_rmse: 0.3700 - val_mape: 1.3561
Epoch 813/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3025 - rmse: 0.4241 - mape: 1.5406 - val_loss: 0.1727 - val_rmse: 0.3237 - val_mape: 1.1755
Epoch 814/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3222 - rmse: 0.4333 - mape: 1.5738 - val_loss: 0.1853 - val_rmse: 0.3384 - val_mape: 1.2304
Epoch 815/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.3010 - rmse: 0.4193 - mape: 1.5218 - val_loss: 0.1519 - val_rmse: 0.2974 - val_mape: 1.0796
Epoch 816/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2908 - rmse: 0.4161 - mape: 1.5106 - val_loss: 0.1590 - val_rmse: 0.3092 - val_mape: 1.1245
Epoch 817/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2934 - rmse: 0.4161 - mape: 1.5098 - val_loss: 0.1753 - val_rmse: 0.3206 - val_mape: 1.1592
Epoch 818/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2865 - rmse: 0.4159 - mape: 1.5110 - val_loss: 0.1694 - val_rmse: 0.3243 - val_mape: 1.1814
Epoch 819/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2938 - rmse: 0.4158 - mape: 1.5106 - val_loss: 0.1566 - val_rmse: 0.3062 - val_mape: 1.1125
Epoch 820/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2858 - rmse: 0.4138 - mape: 1.5022 - val_loss: 0.1761 - val_rmse: 0.3287 - val_mape: 1.1970
Epoch 821/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2876 - rmse: 0.4126 - mape: 1.4968 - val_loss: 0.1587 - val_rmse: 0.3148 - val_mape: 1.1456
Epoch 822/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2722 - rmse: 0.3968 - mape: 1.4424 - val_loss: 0.1478 - val_rmse: 0.3004 - val_mape: 1.0936
Epoch 823/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2773 - rmse: 0.4054 - mape: 1.4715 - val_loss: 0.1674 - val_rmse: 0.3153 - val_mape: 1.1418
Epoch 824/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2634 - rmse: 0.3902 - mape: 1.4157 - val_loss: 0.1602 - val_rmse: 0.3138 - val_mape: 1.1432
Epoch 825/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2756 - rmse: 0.4037 - mape: 1.4648 - val_loss: 0.1784 - val_rmse: 0.3356 - val_mape: 1.2235
Epoch 826/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2593 - rmse: 0.3875 - mape: 1.4047 - val_loss: 0.1534 - val_rmse: 0.3077 - val_mape: 1.1210
Epoch 827/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2794 - rmse: 0.4047 - mape: 1.4668 - val_loss: 0.1562 - val_rmse: 0.3067 - val_mape: 1.1151
Epoch 828/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2610 - rmse: 0.3901 - mape: 1.4135 - val_loss: 0.1537 - val_rmse: 0.3080 - val_mape: 1.1214
Epoch 829/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2549 - rmse: 0.3839 - mape: 1.3919 - val_loss: 0.1585 - val_rmse: 0.3098 - val_mape: 1.1307
Epoch 830/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2398 - rmse: 0.3741 - mape: 1.3586 - val_loss: 0.1671 - val_rmse: 0.3155 - val_mape: 1.1475
Epoch 831/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2764 - rmse: 0.3966 - mape: 1.4362 - val_loss: 0.1923 - val_rmse: 0.3407 - val_mape: 1.2359
Epoch 832/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2492 - rmse: 0.3834 - mape: 1.3914 - val_loss: 0.1817 - val_rmse: 0.3237 - val_mape: 1.1693
Epoch 833/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2609 - rmse: 0.3897 - mape: 1.4117 - val_loss: 0.1705 - val_rmse: 0.3179 - val_mape: 1.1512
Epoch 834/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2487 - rmse: 0.3813 - mape: 1.3826 - val_loss: 0.1684 - val_rmse: 0.3140 - val_mape: 1.1375
Epoch 835/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2477 - rmse: 0.3820 - mape: 1.3836 - val_loss: 0.1578 - val_rmse: 0.3105 - val_mape: 1.1305
Epoch 836/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2418 - rmse: 0.3778 - mape: 1.3694 - val_loss: 0.1691 - val_rmse: 0.3166 - val_mape: 1.1458
Epoch 837/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2514 - rmse: 0.3821 - mape: 1.3833 - val_loss: 0.1547 - val_rmse: 0.3105 - val_mape: 1.1318
Epoch 838/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2256 - rmse: 0.3629 - mape: 1.3141 - val_loss: 0.1629 - val_rmse: 0.3085 - val_mape: 1.1164
Epoch 839/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2212 - rmse: 0.3594 - mape: 1.3030 - val_loss: 0.1540 - val_rmse: 0.3064 - val_mape: 1.1134
Epoch 840/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2364 - rmse: 0.3716 - mape: 1.3454 - val_loss: 0.1571 - val_rmse: 0.3138 - val_mape: 1.1447
Epoch 841/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2384 - rmse: 0.3672 - mape: 1.3291 - val_loss: 0.1652 - val_rmse: 0.3159 - val_mape: 1.1487
Epoch 842/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2323 - rmse: 0.3688 - mape: 1.3374 - val_loss: 0.1579 - val_rmse: 0.3128 - val_mape: 1.1399
Epoch 843/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2195 - rmse: 0.3542 - mape: 1.2831 - val_loss: 0.1604 - val_rmse: 0.3064 - val_mape: 1.1096
Epoch 844/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2319 - rmse: 0.3636 - mape: 1.3161 - val_loss: 0.1898 - val_rmse: 0.3352 - val_mape: 1.2129
Epoch 845/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2182 - rmse: 0.3564 - mape: 1.2914 - val_loss: 0.1527 - val_rmse: 0.3055 - val_mape: 1.1112
Epoch 846/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2369 - rmse: 0.3697 - mape: 1.3390 - val_loss: 0.1444 - val_rmse: 0.2967 - val_mape: 1.0803
Epoch 847/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2264 - rmse: 0.3605 - mape: 1.3042 - val_loss: 0.1611 - val_rmse: 0.3139 - val_mape: 1.1410
Epoch 848/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2349 - rmse: 0.3682 - mape: 1.3320 - val_loss: 0.1464 - val_rmse: 0.2992 - val_mape: 1.0876
Epoch 849/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2181 - rmse: 0.3510 - mape: 1.2695 - val_loss: 0.1525 - val_rmse: 0.3059 - val_mape: 1.1110
Epoch 850/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2199 - rmse: 0.3564 - mape: 1.2906 - val_loss: 0.1548 - val_rmse: 0.3057 - val_mape: 1.1096
Epoch 851/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2172 - rmse: 0.3527 - mape: 1.2765 - val_loss: 0.1471 - val_rmse: 0.2971 - val_mape: 1.0805
Epoch 852/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1969 - rmse: 0.3366 - mape: 1.2185 - val_loss: 0.1528 - val_rmse: 0.3013 - val_mape: 1.0930
Epoch 853/1000
75/75 [==============================] - 0s 6ms/step - loss: 0.2099 - rmse: 0.3464 - mape: 1.2547 - val_loss: 0.1494 - val_rmse: 0.3018 - val_mape: 1.0968
Epoch 854/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2102 - rmse: 0.3492 - mape: 1.2630 - val_loss: 0.1733 - val_rmse: 0.3178 - val_mape: 1.1495
Epoch 855/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1945 - rmse: 0.3304 - mape: 1.1942 - val_loss: 0.1566 - val_rmse: 0.3087 - val_mape: 1.1231
Epoch 856/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1941 - rmse: 0.3324 - mape: 1.2026 - val_loss: 0.1798 - val_rmse: 0.3256 - val_mape: 1.1761
Epoch 857/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2100 - rmse: 0.3429 - mape: 1.2402 - val_loss: 0.1559 - val_rmse: 0.3102 - val_mape: 1.1320
Epoch 858/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2023 - rmse: 0.3432 - mape: 1.2419 - val_loss: 0.1733 - val_rmse: 0.3220 - val_mape: 1.1673
Epoch 859/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1945 - rmse: 0.3321 - mape: 1.2006 - val_loss: 0.1636 - val_rmse: 0.3127 - val_mape: 1.1343
Epoch 860/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.2014 - rmse: 0.3336 - mape: 1.2051 - val_loss: 0.1749 - val_rmse: 0.3212 - val_mape: 1.1627
Epoch 861/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1832 - rmse: 0.3247 - mape: 1.1753 - val_loss: 0.1511 - val_rmse: 0.3023 - val_mape: 1.0994
Epoch 862/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1951 - rmse: 0.3318 - mape: 1.1992 - val_loss: 0.1541 - val_rmse: 0.3040 - val_mape: 1.1041
Epoch 863/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1855 - rmse: 0.3247 - mape: 1.1757 - val_loss: 0.1579 - val_rmse: 0.3095 - val_mape: 1.1251
Epoch 864/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1908 - rmse: 0.3286 - mape: 1.1891 - val_loss: 0.1583 - val_rmse: 0.3081 - val_mape: 1.1198
Epoch 865/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1896 - rmse: 0.3303 - mape: 1.1959 - val_loss: 0.1515 - val_rmse: 0.3026 - val_mape: 1.1028
Epoch 866/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1782 - rmse: 0.3156 - mape: 1.1415 - val_loss: 0.1562 - val_rmse: 0.3044 - val_mape: 1.1048
Epoch 867/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1758 - rmse: 0.3159 - mape: 1.1428 - val_loss: 0.1669 - val_rmse: 0.3101 - val_mape: 1.1228
Epoch 868/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1882 - rmse: 0.3251 - mape: 1.1756 - val_loss: 0.1538 - val_rmse: 0.3036 - val_mape: 1.1023
Epoch 869/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1717 - rmse: 0.3107 - mape: 1.1240 - val_loss: 0.1593 - val_rmse: 0.3065 - val_mape: 1.1109
Epoch 870/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1769 - rmse: 0.3118 - mape: 1.1261 - val_loss: 0.1573 - val_rmse: 0.3111 - val_mape: 1.1340
Epoch 871/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1808 - rmse: 0.3195 - mape: 1.1547 - val_loss: 0.1528 - val_rmse: 0.3048 - val_mape: 1.1083
Epoch 872/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1753 - rmse: 0.3148 - mape: 1.1384 - val_loss: 0.1489 - val_rmse: 0.3048 - val_mape: 1.1109
Epoch 873/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1864 - rmse: 0.3272 - mape: 1.1856 - val_loss: 0.1579 - val_rmse: 0.3046 - val_mape: 1.1050
Epoch 874/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1612 - rmse: 0.3058 - mape: 1.1075 - val_loss: 0.1605 - val_rmse: 0.3115 - val_mape: 1.1338
Epoch 875/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1690 - rmse: 0.3101 - mape: 1.1226 - val_loss: 0.1685 - val_rmse: 0.3147 - val_mape: 1.1386
Epoch 876/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1633 - rmse: 0.2984 - mape: 1.0788 - val_loss: 0.1603 - val_rmse: 0.3086 - val_mape: 1.1176
Epoch 877/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1740 - rmse: 0.3159 - mape: 1.1433 - val_loss: 0.1512 - val_rmse: 0.3044 - val_mape: 1.1081
Epoch 878/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1547 - rmse: 0.2949 - mape: 1.0671 - val_loss: 0.1544 - val_rmse: 0.3038 - val_mape: 1.1030
Epoch 879/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1549 - rmse: 0.2970 - mape: 1.0750 - val_loss: 0.1573 - val_rmse: 0.3046 - val_mape: 1.1044
Epoch 880/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1627 - rmse: 0.3016 - mape: 1.0914 - val_loss: 0.1536 - val_rmse: 0.3059 - val_mape: 1.1139
Epoch 881/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1608 - rmse: 0.3004 - mape: 1.0870 - val_loss: 0.1577 - val_rmse: 0.3062 - val_mape: 1.1109
Epoch 882/1000
75/75 [==============================] - 1s 7ms/step - loss: 0.1574 - rmse: 0.2986 - mape: 1.0808 - val_loss: 0.1704 - val_rmse: 0.3160 - val_mape: 1.1451
Epoch 883/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1570 - rmse: 0.2962 - mape: 1.0717 - val_loss: 0.1487 - val_rmse: 0.3029 - val_mape: 1.1031
Epoch 884/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1505 - rmse: 0.2915 - mape: 1.0559 - val_loss: 0.1615 - val_rmse: 0.3106 - val_mape: 1.1272
Epoch 885/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1585 - rmse: 0.2972 - mape: 1.0756 - val_loss: 0.1527 - val_rmse: 0.3044 - val_mape: 1.1070
Epoch 886/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1518 - rmse: 0.2909 - mape: 1.0535 - val_loss: 0.1538 - val_rmse: 0.3043 - val_mape: 1.1052
Epoch 887/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1509 - rmse: 0.2877 - mape: 1.0417 - val_loss: 0.1513 - val_rmse: 0.3007 - val_mape: 1.0947
Epoch 888/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1442 - rmse: 0.2820 - mape: 1.0217 - val_loss: 0.1684 - val_rmse: 0.3151 - val_mape: 1.1428
Epoch 889/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1492 - rmse: 0.2871 - mape: 1.0406 - val_loss: 0.1556 - val_rmse: 0.3071 - val_mape: 1.1189
Epoch 890/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1465 - rmse: 0.2874 - mape: 1.0428 - val_loss: 0.1672 - val_rmse: 0.3178 - val_mape: 1.1541
Epoch 891/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1458 - rmse: 0.2837 - mape: 1.0291 - val_loss: 0.1569 - val_rmse: 0.3097 - val_mape: 1.1292
Epoch 892/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1522 - rmse: 0.2934 - mape: 1.0639 - val_loss: 0.1506 - val_rmse: 0.3039 - val_mape: 1.1083
Epoch 893/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1499 - rmse: 0.2930 - mape: 1.0635 - val_loss: 0.1570 - val_rmse: 0.3133 - val_mape: 1.1426
Epoch 894/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1536 - rmse: 0.2908 - mape: 1.0539 - val_loss: 0.1528 - val_rmse: 0.3090 - val_mape: 1.1285
Epoch 895/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1451 - rmse: 0.2838 - mape: 1.0288 - val_loss: 0.1542 - val_rmse: 0.3088 - val_mape: 1.1241
Epoch 896/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1453 - rmse: 0.2870 - mape: 1.0413 - val_loss: 0.1585 - val_rmse: 0.3111 - val_mape: 1.1319
Epoch 897/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1515 - rmse: 0.2890 - mape: 1.0478 - val_loss: 0.1599 - val_rmse: 0.3158 - val_mape: 1.1526
Epoch 898/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1489 - rmse: 0.2861 - mape: 1.0381 - val_loss: 0.1759 - val_rmse: 0.3268 - val_mape: 1.1884
Epoch 899/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1511 - rmse: 0.2898 - mape: 1.0516 - val_loss: 0.1573 - val_rmse: 0.3114 - val_mape: 1.1353
Epoch 900/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1405 - rmse: 0.2804 - mape: 1.0175 - val_loss: 0.1647 - val_rmse: 0.3176 - val_mape: 1.1557
Epoch 901/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1463 - rmse: 0.2870 - mape: 1.0411 - val_loss: 0.1662 - val_rmse: 0.3177 - val_mape: 1.1552
Epoch 902/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1394 - rmse: 0.2780 - mape: 1.0096 - val_loss: 0.1990 - val_rmse: 0.3434 - val_mape: 1.2442
Epoch 903/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1396 - rmse: 0.2784 - mape: 1.0119 - val_loss: 0.1800 - val_rmse: 0.3286 - val_mape: 1.1945
Epoch 904/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1421 - rmse: 0.2778 - mape: 1.0099 - val_loss: 0.1599 - val_rmse: 0.3121 - val_mape: 1.1367
Epoch 905/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1379 - rmse: 0.2762 - mape: 1.0039 - val_loss: 0.1559 - val_rmse: 0.3093 - val_mape: 1.1290
Epoch 906/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1445 - rmse: 0.2843 - mape: 1.0318 - val_loss: 0.1572 - val_rmse: 0.3050 - val_mape: 1.1118
Epoch 907/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1381 - rmse: 0.2760 - mape: 1.0033 - val_loss: 0.1815 - val_rmse: 0.3262 - val_mape: 1.1841
Epoch 908/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1417 - rmse: 0.2788 - mape: 1.0136 - val_loss: 0.1562 - val_rmse: 0.3110 - val_mape: 1.1351
Epoch 909/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1305 - rmse: 0.2695 - mape: 0.9803 - val_loss: 0.1802 - val_rmse: 0.3243 - val_mape: 1.1776
Epoch 910/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1356 - rmse: 0.2700 - mape: 0.9817 - val_loss: 0.1539 - val_rmse: 0.3101 - val_mape: 1.1331
Epoch 911/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1498 - rmse: 0.2843 - mape: 1.0334 - val_loss: 0.1638 - val_rmse: 0.3202 - val_mape: 1.1731
Epoch 912/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1369 - rmse: 0.2710 - mape: 0.9858 - val_loss: 0.1646 - val_rmse: 0.3222 - val_mape: 1.1801
Epoch 913/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1424 - rmse: 0.2824 - mape: 1.0277 - val_loss: 0.1660 - val_rmse: 0.3171 - val_mape: 1.1540
Epoch 914/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1410 - rmse: 0.2734 - mape: 0.9947 - val_loss: 0.1599 - val_rmse: 0.3116 - val_mape: 1.1362
Epoch 915/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1423 - rmse: 0.2800 - mape: 1.0194 - val_loss: 0.1583 - val_rmse: 0.3092 - val_mape: 1.1298
Epoch 916/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1376 - rmse: 0.2742 - mape: 0.9976 - val_loss: 0.1644 - val_rmse: 0.3186 - val_mape: 1.1615
Epoch 917/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1398 - rmse: 0.2768 - mape: 1.0081 - val_loss: 0.1704 - val_rmse: 0.3191 - val_mape: 1.1614
Epoch 918/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1353 - rmse: 0.2760 - mape: 1.0049 - val_loss: 0.1569 - val_rmse: 0.3109 - val_mape: 1.1392
Epoch 919/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1510 - rmse: 0.2895 - mape: 1.0542 - val_loss: 0.1592 - val_rmse: 0.3100 - val_mape: 1.1348
Epoch 920/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1435 - rmse: 0.2763 - mape: 1.0070 - val_loss: 0.1585 - val_rmse: 0.3098 - val_mape: 1.1318
Epoch 921/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1385 - rmse: 0.2752 - mape: 1.0020 - val_loss: 0.1839 - val_rmse: 0.3274 - val_mape: 1.1893
Epoch 922/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1347 - rmse: 0.2711 - mape: 0.9877 - val_loss: 0.1584 - val_rmse: 0.3106 - val_mape: 1.1339
Epoch 923/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1393 - rmse: 0.2768 - mape: 1.0084 - val_loss: 0.1661 - val_rmse: 0.3233 - val_mape: 1.1854
Epoch 924/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1374 - rmse: 0.2729 - mape: 0.9939 - val_loss: 0.1545 - val_rmse: 0.3064 - val_mape: 1.1211
Epoch 925/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1297 - rmse: 0.2660 - mape: 0.9706 - val_loss: 0.1579 - val_rmse: 0.3102 - val_mape: 1.1330
Epoch 926/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1347 - rmse: 0.2713 - mape: 0.9890 - val_loss: 0.1812 - val_rmse: 0.3316 - val_mape: 1.2074
Epoch 927/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1342 - rmse: 0.2693 - mape: 0.9816 - val_loss: 0.1607 - val_rmse: 0.3126 - val_mape: 1.1420
Epoch 928/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1307 - rmse: 0.2643 - mape: 0.9642 - val_loss: 0.1716 - val_rmse: 0.3208 - val_mape: 1.1685
Epoch 929/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1379 - rmse: 0.2754 - mape: 1.0038 - val_loss: 0.1633 - val_rmse: 0.3162 - val_mape: 1.1553
Epoch 930/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1330 - rmse: 0.2688 - mape: 0.9801 - val_loss: 0.1859 - val_rmse: 0.3298 - val_mape: 1.1992
Epoch 931/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1423 - rmse: 0.2785 - mape: 1.0146 - val_loss: 0.1674 - val_rmse: 0.3200 - val_mape: 1.1718
Epoch 932/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1324 - rmse: 0.2689 - mape: 0.9804 - val_loss: 0.1626 - val_rmse: 0.3139 - val_mape: 1.1464
Epoch 933/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1295 - rmse: 0.2641 - mape: 0.9638 - val_loss: 0.1842 - val_rmse: 0.3356 - val_mape: 1.2231
Epoch 934/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1427 - rmse: 0.2746 - mape: 1.0009 - val_loss: 0.1747 - val_rmse: 0.3260 - val_mape: 1.1888
Epoch 935/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1289 - rmse: 0.2654 - mape: 0.9688 - val_loss: 0.1663 - val_rmse: 0.3173 - val_mape: 1.1603
Epoch 936/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1427 - rmse: 0.2774 - mape: 1.0108 - val_loss: 0.1691 - val_rmse: 0.3227 - val_mape: 1.1833
Epoch 937/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1326 - rmse: 0.2694 - mape: 0.9842 - val_loss: 0.1664 - val_rmse: 0.3212 - val_mape: 1.1768
Epoch 938/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1450 - rmse: 0.2823 - mape: 1.0291 - val_loss: 0.1596 - val_rmse: 0.3156 - val_mape: 1.1554
Epoch 939/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1295 - rmse: 0.2645 - mape: 0.9648 - val_loss: 0.1718 - val_rmse: 0.3255 - val_mape: 1.1881
Epoch 940/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1332 - rmse: 0.2676 - mape: 0.9757 - val_loss: 0.1734 - val_rmse: 0.3242 - val_mape: 1.1838
Epoch 941/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1438 - rmse: 0.2813 - mape: 1.0260 - val_loss: 0.1700 - val_rmse: 0.3214 - val_mape: 1.1744
Epoch 942/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1366 - rmse: 0.2725 - mape: 0.9953 - val_loss: 0.1628 - val_rmse: 0.3170 - val_mape: 1.1602
Epoch 943/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1353 - rmse: 0.2675 - mape: 0.9768 - val_loss: 0.1851 - val_rmse: 0.3358 - val_mape: 1.2232
Epoch 944/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1364 - rmse: 0.2720 - mape: 0.9931 - val_loss: 0.1664 - val_rmse: 0.3219 - val_mape: 1.1794
Epoch 945/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1476 - rmse: 0.2806 - mape: 1.0219 - val_loss: 0.1756 - val_rmse: 0.3238 - val_mape: 1.1795
Epoch 946/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1405 - rmse: 0.2719 - mape: 0.9916 - val_loss: 0.1805 - val_rmse: 0.3331 - val_mape: 1.2152
Epoch 947/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1363 - rmse: 0.2674 - mape: 0.9753 - val_loss: 0.1653 - val_rmse: 0.3212 - val_mape: 1.1757
Epoch 948/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1345 - rmse: 0.2700 - mape: 0.9856 - val_loss: 0.1596 - val_rmse: 0.3126 - val_mape: 1.1470
Epoch 949/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1300 - rmse: 0.2630 - mape: 0.9598 - val_loss: 0.1615 - val_rmse: 0.3151 - val_mape: 1.1538
Epoch 950/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1365 - rmse: 0.2738 - mape: 0.9992 - val_loss: 0.1671 - val_rmse: 0.3221 - val_mape: 1.1796
Epoch 951/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1380 - rmse: 0.2746 - mape: 1.0026 - val_loss: 0.1824 - val_rmse: 0.3301 - val_mape: 1.2018
Epoch 952/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1393 - rmse: 0.2751 - mape: 1.0039 - val_loss: 0.1662 - val_rmse: 0.3203 - val_mape: 1.1735
Epoch 953/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1357 - rmse: 0.2681 - mape: 0.9780 - val_loss: 0.1785 - val_rmse: 0.3295 - val_mape: 1.2011
Epoch 954/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1421 - rmse: 0.2772 - mape: 1.0111 - val_loss: 0.1658 - val_rmse: 0.3205 - val_mape: 1.1730
Epoch 955/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1336 - rmse: 0.2693 - mape: 0.9834 - val_loss: 0.1776 - val_rmse: 0.3277 - val_mape: 1.1952
Epoch 956/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1427 - rmse: 0.2787 - mape: 1.0171 - val_loss: 0.1644 - val_rmse: 0.3184 - val_mape: 1.1650
Epoch 957/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1351 - rmse: 0.2672 - mape: 0.9747 - val_loss: 0.1779 - val_rmse: 0.3261 - val_mape: 1.1881
Epoch 958/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1451 - rmse: 0.2828 - mape: 1.0322 - val_loss: 0.1629 - val_rmse: 0.3161 - val_mape: 1.1601
Epoch 959/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1401 - rmse: 0.2764 - mape: 1.0095 - val_loss: 0.1728 - val_rmse: 0.3310 - val_mape: 1.2166
Epoch 960/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1349 - rmse: 0.2705 - mape: 0.9888 - val_loss: 0.1776 - val_rmse: 0.3244 - val_mape: 1.1829
Epoch 961/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1380 - rmse: 0.2721 - mape: 0.9927 - val_loss: 0.1664 - val_rmse: 0.3176 - val_mape: 1.1637
Epoch 962/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1291 - rmse: 0.2627 - mape: 0.9599 - val_loss: 0.1680 - val_rmse: 0.3199 - val_mape: 1.1684
Epoch 963/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1361 - rmse: 0.2659 - mape: 0.9697 - val_loss: 0.1677 - val_rmse: 0.3219 - val_mape: 1.1800
Epoch 964/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1279 - rmse: 0.2649 - mape: 0.9682 - val_loss: 0.1692 - val_rmse: 0.3208 - val_mape: 1.1717
Epoch 965/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1340 - rmse: 0.2641 - mape: 0.9645 - val_loss: 0.1733 - val_rmse: 0.3315 - val_mape: 1.2151
Epoch 966/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1455 - rmse: 0.2814 - mape: 1.0268 - val_loss: 0.1710 - val_rmse: 0.3260 - val_mape: 1.1980
Epoch 967/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1318 - rmse: 0.2672 - mape: 0.9763 - val_loss: 0.1651 - val_rmse: 0.3200 - val_mape: 1.1719
Epoch 968/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1313 - rmse: 0.2651 - mape: 0.9684 - val_loss: 0.1796 - val_rmse: 0.3373 - val_mape: 1.2394
Epoch 969/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1434 - rmse: 0.2773 - mape: 1.0129 - val_loss: 0.1747 - val_rmse: 0.3291 - val_mape: 1.2079
Epoch 970/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1344 - rmse: 0.2703 - mape: 0.9876 - val_loss: 0.1665 - val_rmse: 0.3204 - val_mape: 1.1726
Epoch 971/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1419 - rmse: 0.2708 - mape: 0.9878 - val_loss: 0.1707 - val_rmse: 0.3246 - val_mape: 1.1867
Epoch 972/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1395 - rmse: 0.2756 - mape: 1.0057 - val_loss: 0.1862 - val_rmse: 0.3338 - val_mape: 1.2160
Epoch 973/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1373 - rmse: 0.2711 - mape: 0.9893 - val_loss: 0.1709 - val_rmse: 0.3244 - val_mape: 1.1858
Epoch 974/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1285 - rmse: 0.2618 - mape: 0.9552 - val_loss: 0.1657 - val_rmse: 0.3195 - val_mape: 1.1682
Epoch 975/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1279 - rmse: 0.2632 - mape: 0.9609 - val_loss: 0.1644 - val_rmse: 0.3213 - val_mape: 1.1759
Epoch 976/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1282 - rmse: 0.2631 - mape: 0.9579 - val_loss: 0.1562 - val_rmse: 0.3131 - val_mape: 1.1445
Epoch 977/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1467 - rmse: 0.2830 - mape: 1.0294 - val_loss: 0.1867 - val_rmse: 0.3328 - val_mape: 1.2075
Epoch 978/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1263 - rmse: 0.2624 - mape: 0.9551 - val_loss: 0.1617 - val_rmse: 0.3156 - val_mape: 1.1525
Epoch 979/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1140 - rmse: 0.2532 - mape: 0.9220 - val_loss: 0.1637 - val_rmse: 0.3154 - val_mape: 1.1484
Epoch 980/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1295 - rmse: 0.2675 - mape: 0.9719 - val_loss: 0.1637 - val_rmse: 0.3184 - val_mape: 1.1623
Epoch 981/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1188 - rmse: 0.2536 - mape: 0.9230 - val_loss: 0.1610 - val_rmse: 0.3135 - val_mape: 1.1422
Epoch 982/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1230 - rmse: 0.2590 - mape: 0.9406 - val_loss: 0.1747 - val_rmse: 0.3244 - val_mape: 1.1769
Epoch 983/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1227 - rmse: 0.2586 - mape: 0.9391 - val_loss: 0.1715 - val_rmse: 0.3239 - val_mape: 1.1762
Epoch 984/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1245 - rmse: 0.2562 - mape: 0.9312 - val_loss: 0.1832 - val_rmse: 0.3301 - val_mape: 1.1983
Epoch 985/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1282 - rmse: 0.2605 - mape: 0.9461 - val_loss: 0.1608 - val_rmse: 0.3160 - val_mape: 1.1546
Epoch 986/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1183 - rmse: 0.2520 - mape: 0.9142 - val_loss: 0.1601 - val_rmse: 0.3141 - val_mape: 1.1447
Epoch 987/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1194 - rmse: 0.2541 - mape: 0.9222 - val_loss: 0.1672 - val_rmse: 0.3226 - val_mape: 1.1737
Epoch 988/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1141 - rmse: 0.2436 - mape: 0.8867 - val_loss: 0.1604 - val_rmse: 0.3137 - val_mape: 1.1428
Epoch 989/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1270 - rmse: 0.2574 - mape: 0.9351 - val_loss: 0.1755 - val_rmse: 0.3246 - val_mape: 1.1782
Epoch 990/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1163 - rmse: 0.2447 - mape: 0.8903 - val_loss: 0.1614 - val_rmse: 0.3116 - val_mape: 1.1337
Epoch 991/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1144 - rmse: 0.2472 - mape: 0.8972 - val_loss: 0.1580 - val_rmse: 0.3119 - val_mape: 1.1354
Epoch 992/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1221 - rmse: 0.2509 - mape: 0.9126 - val_loss: 0.1721 - val_rmse: 0.3188 - val_mape: 1.1578
Epoch 993/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1188 - rmse: 0.2501 - mape: 0.9093 - val_loss: 0.1649 - val_rmse: 0.3174 - val_mape: 1.1547
Epoch 994/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1166 - rmse: 0.2516 - mape: 0.9144 - val_loss: 0.1562 - val_rmse: 0.3109 - val_mape: 1.1350
Epoch 995/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1282 - rmse: 0.2563 - mape: 0.9305 - val_loss: 0.1605 - val_rmse: 0.3117 - val_mape: 1.1364
Epoch 996/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1158 - rmse: 0.2484 - mape: 0.9040 - val_loss: 0.1654 - val_rmse: 0.3211 - val_mape: 1.1735
Epoch 997/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1228 - rmse: 0.2529 - mape: 0.9188 - val_loss: 0.1675 - val_rmse: 0.3199 - val_mape: 1.1666
Epoch 998/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1242 - rmse: 0.2559 - mape: 0.9316 - val_loss: 0.1662 - val_rmse: 0.3186 - val_mape: 1.1595
Epoch 999/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1243 - rmse: 0.2580 - mape: 0.9389 - val_loss: 0.1671 - val_rmse: 0.3224 - val_mape: 1.1818
Epoch 1000/1000
75/75 [==============================] - 0s 5ms/step - loss: 0.1200 - rmse: 0.2491 - mape: 0.9089 - val_loss: 0.1838 - val_rmse: 0.3343 - val_mape: 1.2139
Test loss(mse) : 0.18378639221191406
Test RMSE : 0.33431476354599
Test MAPE : 1.213940143585205

8. Visualization

In [0]:
import matplotlib.pyplot as plt
import numpy as np
import os

# 모델 학습 후 정보가 담긴 history 내용을 토대로 선 그래프를 그리는 함수 설정

def plot_rmse(history, title=None):        
    # summarize history for RMSE
    if not isinstance(history, dict):
        history = history.history

    plt.plot(history['rmse'])        
    plt.plot(history['val_rmse'])    
    if title is not None:
        plt.title(title)
    plt.ylabel('RMSE')
    plt.xlabel('Epoch')
    plt.legend(['Training data', 'Validation data'], loc=0)
    # plt.show()

def plot_mape(history, title=None):        
    # summarize history for MAPE
    if not isinstance(history, dict):
        history = history.history

    plt.plot(history['mape'])        
    plt.plot(history['val_mape'])    
    if title is not None:
        plt.title(title)
    plt.ylabel('MAPE')
    plt.xlabel('Epoch')
    plt.legend(['Training data', 'Validation data'], loc=0)
    # plt.show()

def plot_loss(history, title=None):     # Loss Visualization
    # summarize history for loss
    if not isinstance(history, dict):
        history = history.history

    plt.plot(history['loss'])           # loss
    plt.plot(history['val_loss'])       # validation
    if title is not None:
        plt.title(title)
    plt.ylabel('Loss')
    plt.xlabel('Epoch')
    plt.legend(['Training data', 'Validation data'], loc=0)
    # plt.show()
In [13]:
# Visualization
plot_loss(history, '(a) Loss')  # 학습 경과에 따른 정확도 변화 추이
plt.show()
plot_rmse(history, '(b) RMSE')     # 학습 경과에 따른 손실값 변화 추이
plt.show()
plot_mape(history, '(c) MAPE')     # 학습 경과에 따른 손실값 변화 추이
plt.show()

9. 학습 모델 저장하기

In [0]:
model.save('cnn_bracket_model.h5') # 모델 아키텍처와 모델 가중치 저장

10. 학습된 모델 불러오기

In [0]:
from tensorflow.keras.models import load_model
model = load_model('cnn_bracket_model.h5', custom_objects={"rmse": rmse})

11. 모델 사용하여 예측하기

In [52]:
import matplotlib.pyplot as plt
%matplotlib inline # notebook을 실행한 브라우저에서 바로 그림을 볼 수 있게 해 줌.

plt.imshow(test_images[0,:,:,0]);
In [61]:
pred = model.predict(test_images)
print(test_labels[0], pred[0])
25.0732 [24.82604]