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<p>我有一个经过训练的模型,它是用Keras创建的。在这个模型中,我想通过冻结除最后一个卷积层以外的所有层来应用转移学习。但是,当我在冻结图层后拟合模型时,我注意到有些冻结的图层具有不同的权重。我怎样才能避免这种情况?在</p>
<p>我试图用<code>model.trainable = False</code>冻结整个模型,但这也没有成功。在</p>
<p>我使用的是python3.5.0、tensorflow 1.0.1和keras2.0.3</p>
<hr/>
<p><strong>示例脚本</strong></p>
<pre><code>import os
import timeit
import datetime
import numpy as np
from keras.layers.core import Activation, Reshape, Permute
from keras.layers.convolutional import Convolution2D, MaxPooling2D, UpSampling2D, ZeroPadding2D
from keras.layers.normalization import BatchNormalization
from keras.optimizers import Adam
from keras import models
from keras import backend as K
K.set_image_dim_ordering('th')
def conv_model(input_shape, data_shape, kern_size, filt_size, pad_size,\
maxpool_size, n_classes, compile_model=True):
"""
Create a small conv neural network
input_shape: input shape of the images
data_shape: 1d shape of the data
kern_size: Kernel size used in all convolutional2d layers
filt_size: Filter size of the first and last convolutional2d layer
pad_size: size of padding
maxpool_size: Pool size of all maxpooling2d and upsampling2d layers
n_classes: number of output classes
compile_model: True if the model should be compiled
output: Keras deep learning model
"""
#keep track of compilation time
start_time = timeit.default_timer()
model = models.Sequential()
# Add a noise layer to get a denoising autoencoder. This helps avoid overfitting
model.add(ZeroPadding2D(padding=(pad_size, pad_size), input_shape=input_shape))
#Encoding layers
model.add(Convolution2D(filt_size, kern_size, kern_size, border_mode='valid'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(maxpool_size, maxpool_size)))
model.add(UpSampling2D(size=(maxpool_size, maxpool_size)))
model.add(ZeroPadding2D(padding=(pad_size, pad_size)))
model.add(Convolution2D(filt_size, kern_size, kern_size, border_mode='valid'))
model.add(BatchNormalization())
model.add(Convolution2D(n_classes, 1, 1, border_mode='valid'))
model.add(Reshape((n_classes, data_shape), input_shape=(n_classes,)+input_shape[1:]))
model.add(Permute((2, 1)))
model.add(Activation('softmax'))
if compile_model:
model.compile(loss="categorical_crossentropy", optimizer='adam', metrics=["accuracy"])
print('Model compiled in {0} seconds'.format(datetime.timedelta(seconds=round(\
timeit.default_timer() - start_time))))
return model
if __name__ == '__main__':
#Create some random training data
train_data = np.random.randint(0, 10, 3*512*512*20, dtype='uint8').reshape(-1, 3, 512, 512)
train_labels = np.random.randint(0, 1, 7*512*512*20, dtype='uint8').reshape(-1, 512*512, 7)
#Get dims of the data
data_dims = train_data.shape[2:]
data_shape = np.prod(data_dims)
#Create initial model
initial_model = conv_model((train_data.shape[1], train_data.shape[2], train_data.shape[3]),\
data_shape, 3, 4, 1, 2, train_labels.shape[-1])
#Train initial model on first part of the training data
initial_model.fit(train_data[0:10], train_labels[0:10], verbose=2)
#Store initial weights
initial_weights = initial_model.get_weights()
#Create transfer learning model
transf_model = conv_model((train_data.shape[1], train_data.shape[2], train_data.shape[3]),\
data_shape, 3, 4, 1, 2, train_labels.shape[-1], False)
#Set transfer model weights
transf_model.set_weights(initial_weights)
#Set all layers trainable to False (except final conv layer)
for layer in transf_model.layers:
layer.trainable = False
transf_model.layers[9].trainable = True
print(transf_model.layers[9])
#Compile model
transf_model.compile(loss="categorical_crossentropy", optimizer=Adam(lr=1e-4),\
metrics=["accuracy"])
#Train model on second part of the data
transf_model.fit(train_data[10:20], train_labels[10:20], verbose=2)
#Store transfer model weights
transf_weights = transf_model.get_weights()
#Check where the weights have changed
for i in range(len(initial_weights)):
update_w = np.sum(initial_weights[i] != transf_weights[i])
if update_w != 0:
print(str(update_w)+' updated weights for layer '+str(transf_model.layers[i]))
</code></pre>