【发布时间】：2018-11-12 11:46:26
【问题描述】：
我知道从自动编码器中提取的特征可以输入到 mlp 中以用于分类或回归目的。这是我之前做过的事情。
但是如果我有 2 个自动编码器呢？我可以从 2 个自动编码器的瓶颈层中提取特征并将它们输入到基于这些特征执行分类的 mlp 中吗？如果是，那么如何？我不确定如何连接这两个功能集。我尝试使用 numpy.hstack() 给我“不可散列切片”错误，而使用 tf.concat() 给我错误“模型的输入张量必须是 Keras 张量”。两个自动编码器的瓶颈层的维度均为 (None,100)。所以，基本上，如果我将它们水平堆叠，我应该得到一个（无，200）。 mlp 的隐藏层可能包含一些 (num_hidden=100) 个神经元。有人可以帮忙吗？
x1 = autoencoder1.get_layer('encoder2').output
x2 = autoencoder2.get_layer('encoder2').output
#inp = np.hstack((x1, x2))
inp = tf.concat([x1, x2], 1)
x = tf.concat([x1, x2], 1)
h = Dense(num_hidden, activation='relu', name='hidden')(x)
y = Dense(1, activation='sigmoid', name='prediction')(h)
mymlp = Model(inputs=inp, outputs=y)
# Compile model
mymlp.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Train model
mymlp.fit(x_train, y_train, epochs=20, batch_size=8)
根据@twolffpiggott 的建议更新：
from keras.layers import Input, Dense, Dropout
from keras import layers
from keras.models import Model
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
import numpy as np
x1 = Data1
x2 = Data2
y = Data3
num_neurons1 = x1.shape[1]
num_neurons2 = x2.shape[1]
# Train-test split
x1_train, x1_test, x2_train, x2_test, y_train, y_test = train_test_split(x1, x2, y, test_size=0.2)
# scale data within [0-1] range
scalar = MinMaxScaler()
x1_train = scalar.fit_transform(x1_train)
x1_test = scalar.transform(x1_test)
x2_train = scalar.fit_transform(x2_train)
x2_test = scalar.transform(x2_test)
x_train = np.concatenate([x1_train, x2_train], axis =-1)
x_test = np.concatenate([x1_test, x2_test], axis =-1)
# Auto-encoder1
encoding_dim1 = 500
encoding_dim2 = 100
input_data = Input(shape=(num_neurons1,))
encoded = Dense(encoding_dim1, activation='relu', name='encoder1')(input_data)
encoded1 = Dense(encoding_dim2, activation='relu', name='encoder2')(encoded)
decoded = Dense(encoding_dim2, activation='relu', name='decoder1')(encoded1)
decoded = Dense(num_neurons1, activation='sigmoid', name='decoder2')(decoded)
# this model maps an input to its reconstruction
autoencoder1 = Model(inputs=input_data, outputs=decoded)
autoencoder1.compile(optimizer='sgd', loss='mse')
# training
autoencoder1.fit(x1_train, x1_train,
epochs=100,
batch_size=8,
shuffle=True,
validation_data=(x1_test, x1_test))
# Auto-encoder2
encoding_dim1 = 500
encoding_dim2 = 100
input_data = Input(shape=(num_neurons2,))
encoded = Dense(encoding_dim1, activation='relu', name='encoder1')(input_data)
encoded2 = Dense(encoding_dim2, activation='relu', name='encoder2')(encoded)
decoded = Dense(encoding_dim2, activation='relu', name='decoder1')(encoded2)
decoded = Dense(num_neurons2, activation='sigmoid', name='decoder2')(decoded)
# this model maps an input to its reconstruction
autoencoder2 = Model(inputs=input_data, outputs=decoded)
autoencoder2.compile(optimizer='sgd', loss='mse')
# training
autoencoder2.fit(x2_train, x2_train,
epochs=100,
batch_size=8,
shuffle=True,
validation_data=(x2_test, x2_test))
# MLP
num_hidden = 100
encoded1.trainable = False
encoded2.trainable = False
encoded1 = autoencoder1(autoencoder1.inputs)
encoded2 = autoencoder2(autoencoder2.inputs)
concatenated = layers.concatenate([encoded1, encoded2], axis=-1)
x = Dropout(0.2)(concatenated)
h = Dense(num_hidden, activation='relu', name='hidden')(x)
h = Dropout(0.5)(h)
y = Dense(1, activation='sigmoid', name='prediction')(h)
myMLP = Model(inputs=[autoencoder1.inputs, autoencoder2.inputs], outputs=y)
# Compile model
myMLP.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Training
myMLP.fit(x_train, y_train, epochs=200, batch_size=8)
# Testing
myMLP.predict(x_test)
给我一个错误：unhashable type: 'list' from the line: myMLP = 模型（输入=[autoencoder1.inputs，autoencoder2.inputs]，输出=y）
【问题讨论】：
标签： python neural-network keras autoencoder