Question

我训练并加载了cnn +密集模型：

# load model
cnn_model = load_model('my_cnn_model.h5')
cnn_model.summary()

输出是这样（我的图像尺寸为2 X 3600）：

_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
conv2d_1 (Conv2D)            (None, 2, 3600, 32)       128
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 2, 1800, 32)       3104
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 2, 600, 32)        0
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 2, 600, 64)        6208
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 2, 300, 64)        12352
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 2, 100, 64)        0
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 2, 100, 128)       24704
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 2, 50, 128)        49280
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 2, 16, 128)        0
_________________________________________________________________
flatten_1 (Flatten)          (None, 4096)              0
_________________________________________________________________
dense_1 (Dense)              (None, 1024)              4195328
_________________________________________________________________
dense_2 (Dense)              (None, 1024)              1049600
_________________________________________________________________
dense_3 (Dense)              (None, 3)                 3075
=================================================================
Total params: 5,343,779
Trainable params: 5,343,779
Non-trainable params: 0

现在，我要保留的重量是扁平的，并用LSTM替换密集层以训练添加的LSTM零件。

我刚刚写道：

# freeze model
base_model = cnn_model(input_shape=(2, 3600, 1))

#base_model = cnn_model
base_model.trainable = False

# Adding the first lstm layer
x = LSTM(1024,activation='relu',return_sequences='True')(base_model.output)

# Adding the second lstm layer
x = LSTM(1024, activation='relu',return_sequences='False')(x)

# Adding the output
output = Dense(3,activation='linear')(x)

# Final model creation
model = Model(inputs=[base_model.input], outputs=[output])

但是我得到了：

base_model = cnn_model(input_shape=(2, 3600, 1))
TypeError: __call__() missing 1 required positional argument: 'inputs'

我知道我必须在Flatten层中理想地添加TimeDistributed，但是我不知道该怎么做。此外，我不确定base_model.trainable = False是否确实符合我的要求。你能帮我做这份工作吗？

非常感谢您！

Answer 1

您不能直接从Flatten（）中获取输出，LSTM需要2维特征（时间，滤波器）。您必须重塑张量。

您可以在展平（最大池）之前从该层获取输出，假设该层在模型中具有索引i，我们可以从该层获取输出并根据需要对输出进行整形，将其传递给LSTM。

before_flatten = base_model.layers[i].output # i is the index of the layer from which you want to take the model output

conv2lstm_reshape = Reshape((-1, 2))(before_flatten) # you have to select it, the temporal dim and filters

# Adding the first lstm layer
x = LSTM(1024,activation='relu',return_sequences='True')(conv2lstm_reshape)

# Adding the second lstm layer
x = LSTM(1024, activation='relu',return_sequences='False')(x)

# Adding the output
output = Dense(3,activation='linear')(before_flatten)

# Final model creation
model = Model(inputs=[base_model.input], outputs=[output])

model.summary()

KERAS：预训练了CNN + Dense模型。如何冻结CNN权重并用LSTM替代Dense？

1 个答案: