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我想在 LSTM 上稍微改变我的模型架构，以便它接受与全连接方法完全相同的扁平化输入。

来自 Keras 示例的工作 Dnn 模型

import keras

from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.utils import to_categorical

# import the data
from keras.datasets import mnist

# read the data
(x_train, y_train), (x_test, y_test) = mnist.load_data()

num_pixels = x_train.shape[1] * x_train.shape[2] # find size of one-dimensional vector

x_train = x_train.reshape(x_train.shape[0], num_pixels).astype('float32') # flatten training images
x_test = x_test.reshape(x_test.shape[0], num_pixels).astype('float32') # flatten test images

# normalize inputs from 0-255 to 0-1
x_train = x_train / 255
x_test = x_test / 255

# one hot encode outputs
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

num_classes = y_test.shape[1]
print(num_classes)



# define classification model
def classification_model():
    # create model
    model = Sequential()
    model.add(Dense(num_pixels, activation='relu', input_shape=(num_pixels,)))
    model.add(Dense(100, activation='relu'))
    model.add(Dense(num_classes, activation='softmax'))


    # compile model
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model


# build the model
model = classification_model()

# fit the model
model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=10, verbose=2)

# evaluate the model
scores = model.evaluate(x_test, y_test, verbose=0)

同样的问题，但尝试 LSTM（仍然有语法错误）

def kaggle_LSTM_model():
    model = Sequential()
    model.add(LSTM(128, input_shape=(x_train.shape[1:]), activation='relu', return_sequences=True))
    # What does return_sequences=True do?
    model.add(Dropout(0.2))

    model.add(Dense(32, activation='relu'))
    model.add(Dropout(0.2))

    model.add(Dense(10, activation='softmax'))

    opt = tf.keras.optimizers.Adam(lr=1e-3, decay=1e-5)
    model.compile(loss='sparse_categorical_crossentropy', optimizer=opt,
             metrics=['accuracy'])

    return model

model_kaggle_LSTM = kaggle_LSTM_model()

# fit the model
model_kaggle_LSTM.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=10, verbose=2)

# evaluate the model
scores = model_kaggle_LSTM.evaluate(x_test, y_test, verbose=0)

问题在这里：

model.add(LSTM(128, input_shape=(x_train.shape[1:]), activation='relu', return_sequences=True))

ValueError：输入 0 与层 lstm_17 不兼容：预期 ndim=3，发现 ndim=2

如果我回去不压平 x_train 和 y_train，它会起作用。但是，我希望这只是“另一种模型选择”，它以相同的预处理输入为基础。我认为传递 shape[1:] 会像真正扁平的 input_shape 一样起作用。我敢肯定，我很容易错过维度，但是经过一个小时的摆弄和调试后我无法得到它，尽管确实发现没有将 28x28 展平为 784 的作品，但我不明白为什么作品。非常感谢！

对于奖励积分，最好举一个例子来说明如何在 1D (784,) 或 2D (28, 28) 中执行 DNN 或 LSTM。

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1 回答 1

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RNN layers such as LSTM are meant for sequence processing (i.e. a series of vectors which their order of appearance matters). You can look at an image from top to bottom, and consider each row of pixels as a vector. Therefore, the image would be a sequence of vectors and can be fed to the RNN layer. Therefore, according to this description, you should expect that the RNN layer take an input of shape (sequence_length, number_of_features). That's why when you feed the images to the LSTM network in their original shape, i.e. (28,28), it works.

Now if you insist on feeding the LSTM model the flattened image, i.e. with shape (784,), you have at least two options: either you can consider this as a sequence of length one, i.e. (1, 748), which does not make much sense; or you can add a Reshape layer to your model to reshape back the input to its original shape suitable for the input shape of a LSTM layer, like this:

from keras.layers import Reshape

def kaggle_LSTM_model():
    model = Sequential()
    model.add(Reshape((28,28), input_shape=x_train.shape[1:]))
    # the rest is the same...
于 2019-03-26T17:33:05.567 回答