4
出力の次元が入力の次元と異なる場合、Kerasでエラーが発生します。私は20がどこから来るのか分かりません。私のすべての次元は18と表示されているようです。また、convolution2d_70の出力シェイプでは 'multiple'と表示されるので、その意味がわかりません。何か案は？Kerasの出力レイヤの形状がCNNの入力と比較して一致しません
例外：モデルターゲットエラーチェック：形状を有することが期待convolution2d_70（なし、1、36L、20L）が、形状のアレイ（49L、1L、36L、18L）
from keras.layers import Dense, Input, Convolution2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from os import listdir
import os.path
import numpy as np
import re
versions = !pip freeze
for line in versions:
if re.search('Keras', line):
print line
samples = 100
x = np.ndarray([samples,36,18])
i=0
for i in range(samples):
x[i] = np.random.randint(15, size=(36, 18))
i+=1
#TODO: CREATE A REAL TEST SET
x_train = x[:49]
x_test = x[50:]
print x_train.shape
print x_test.shape
#INPUT LAYER
input_img = Input(shape=(1,x_train.shape[1],x_train.shape[2]))
x = Convolution2D(16, 3, 3, activation='relu', border_mode='same')(input_img)
x = MaxPooling2D((2, 2), border_mode='same')(x)
x = Convolution2D(8, 3, 3, activation='relu', border_mode='same')(x)
x = MaxPooling2D((2, 2), border_mode='same')(x)
x = Convolution2D(8, 3, 3, activation='relu', border_mode='same')(x)
encoded = MaxPooling2D((2, 2), border_mode='same')(x)
# at this point the representation is (8, 4, 4) i.e. 128-dimensional
x = Convolution2D(8, 3, 3, activation='relu', border_mode='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Convolution2D(8, 3, 3, activation='relu', border_mode='same')(x)
x = UpSampling2D((2, 2))(x)
x = Convolution2D(16, 3, 3, activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Convolution2D(1, 3, 3, activation='sigmoid', border_mode='same')(x)
#MODEL
autoencoder = Model(input=input_img, output=decoded)
#SEPERATE ENCODER MODEL
encoder = Model(input=input_img, output=encoded)
# create a placeholder for an encoded (32-dimensional) input
encoded_input = Input(shape=(8, 4, 4))
# retrieve the last layer of the autoencoder model
decoder_layer1 = autoencoder.layers[-3]
decoder_layer2 = autoencoder.layers[-2]
decoder_layer3 = autoencoder.layers[-1]
print decoder_layer3.get_config()
# create the decoder model
decoder = Model(input=encoded_input, output=decoder_layer3(decoder_layer2(decoder_layer1(encoded_input))))
#COMPILER
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
autoencoder.summary()
x_train = x_train.astype('float32')/15.
x_test = x_test.astype('float32')/15.
x_test = np.reshape(x_test, (len(x_test), 1, x_test.shape[1], x_test.shape[2]))
x_train = np.reshape(x_train, (len(x_train), 1, x_train.shape[1], x_train.shape[2]))
autoencoder.fit(x_train,
x_train,
nb_epoch=5,
batch_size=1,
verbose=True,
shuffle=True,
validation_data=(x_test,x_test))