我正在尝试执行CNN的分类任务。我想看看每个历元的权重是如何优化的。为此,我需要倒数第二层的值。另外,我将硬编码最后一层和反向传播自己。也请推荐一些有帮助的API。在
编辑:我添加了来自keras示例的代码。期待编辑它。 This链接提供了一些提示。我已经提到了我需要输出的层。在
from __future__ import print_function
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding
from keras.layers import Conv1D, GlobalMaxPooling1D
from keras.datasets import imdb
# set parameters:
max_features = 5000
maxlen = 400
batch_size = 100
embedding_dims = 50
filters = 250
kernel_size = 3
hidden_dims = 250
epochs = 100
print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')
print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
print('Build model...')
model = Sequential()
# we start off with an efficient embedding layer which maps
# our vocab indices into embedding_dims dimensions
model.add(Embedding(max_features,
embedding_dims,
input_length=maxlen))
model.add(Dropout(0.2))
# we add a Convolution1D, which will learn filters
# word group filters of size filter_length:
model.add(Conv1D(filters,
kernel_size,
padding='valid',
activation='relu',
strides=1))
# we use max pooling:
model.add(GlobalMaxPooling1D())
# We add a vanilla hidden layer:
model.add(Dense(hidden_dims))
model.add(Dropout(0.2))
model.add(Activation('relu'))
# We project onto a single unit output layer, and squash it with a sigmoid:
model.add(Dense(1))
model.add(Activation('sigmoid')) #<======== I need output after this.
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test))
您可以这样获取模型的各个层:
要获得层的权重,请使用
^{pr2}$layer.get_weights()
,如下所示:要获得最后一层的计算值,请使用
model.predict()
:要获得其他层的评估,请使用tensorflow执行以下操作:
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