我在试着理解Google的colab代码。我应该如何使用这个代码:
from keras import backend as K
prediction_model = lstm_model(seq_len=1, batch_size=BATCH_SIZE, stateful=True)
prediction_model.load_weights('/tmp/bard.h5')
get_test_layer_output = K.function([prediction_model.layers[0].input],
[prediction_model.layers[1].output])
layer_output = get_test_layer_output([x])[0]要查看每一层之后的值?或者有什么不同的方法来查看值(而不是形状)?
Layer (type) Output Shape Param #
=================================================================
seed (InputLayer) (128, 100) 0
_________________________________________________________________
embedding (Embedding) (128, 100, 512) 131072
_________________________________________________________________
lstm (LSTM) (128, 100, 512) 2099200
_________________________________________________________________
lstm_1 (LSTM) (128, 100, 512) 2099200
_________________________________________________________________
time_distributed (TimeDistri (128, 100, 256) 131328
=================================================================
Total params: 4,460,800
Trainable params: 4,460,800
Non-trainable params: 0发布于 2019-05-24 04:17:49
对于要在Keras模型的层上执行的任何操作,首先需要访问模型所包含的keras.layers对象的列表。
model_layers = model.layers这个列表中的每个层对象都有自己的input和output张量(如果使用TensorFlow后端)
input_tensor = model.layers[ layer_index ].input
output_tensor = model.layers[ layer_index ].output如果您直接使用output_tensor方法运行tf.Session.run(),您将得到一个错误,说明在访问层的输出之前,必须将输入提供给模型。
import tensorflow as tf
import numpy as np
layer_index = 3 # The index of the layer whose output needs to be fetched
model = tf.keras.models.load_model( 'model.h5' )
out_ten = model.layers[ layer_index ].output
with tf.Session() as sess:
tf.global_variables_initializer().run()
output = sess.run( out_ten , { model.input : np.ones((2,186))} )
print( output )在运行模型之前,需要使用tf.global_variables_initializer().run()初始化变量。model.input为模型的输入提供占位符张量。
https://stackoverflow.com/questions/56282323
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