我正试图了解Google's colab code。我应该如何使用此代码:
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]
查看每一层之后的值?还是有其他方法可以查看值(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
答案 0 :(得分:0)
对于要在Keras模型层上进行的任何操作,首先,我们需要访问模型所保存的keras.layers
对象的列表。
model_layers = model.layers
此列表中的每个Layer对象都有其自己的input
和output
张量(如果您使用的是TensorFlow后端)
input_tensor = model.layers[ layer_index ].input
output_tensor = model.layers[ layer_index ].output
如果您使用tf.Session.run()
方法直接运行output_tensor,则会出现错误,指出在访问层的输出之前必须将输入馈送到模型中。
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,您需要使用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。