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我在Keras中有以下模型,TimeDistributed(Flatten())(x)不起作用,它给出与输出相同的形状。我在Windows 10上使用带有Tensorflow后端和Python 3.5.3的Keras的最新版本。我做错了什么?有其他解决方案吗?TimeDistributed(Flatten())在Keras中给出相同的输出形状
rnn_size = 128
input_tensor = Input((width, height, 3))
x = input_tensor
x = Convolution2D(32, 3, 3, activation='relu', input_shape=[width, height, 3])(x)
x = Convolution2D(32, 3, 3, activation='relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Convolution2D(32, 3, 3, activation='relu')(x)
x = Convolution2D(32, 3, 3, activation='relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Convolution2D(32, 3, 3, activation='relu')(x)
x = Convolution2D(32, 3, 3, activation='relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
conv_shape = x.get_shape()
x = Reshape(target_shape = (int(conv_shape [1]), int(conv_shape[2] * conv_shape[3])))(x)
x = Dense(32, activation='relu')(x)
x = GRU(rnn_size, return_sequences=True, init='he_normal', name='gru1')(x)
x = TimeDistributed(Flatten())(x)
x = TimeDistributed(Dropout(0.25))(x)
x = TimeDistributed(Dense(n_class, init='he_normal', activation='softmax'))(x)
model = Model(input = [input_tensor], output = [x])
model.compile(loss='categorical_crossentropy', optimizer='adadelta')