我正在尝试获取Keras中自定义RNN模型每一层的输出。该模型的代码如下。
from tensorflow.python import keras
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.layers import RNN, Dense, Activation
from tensorflow.python.keras.models import Model
import numpy as np
class MinimalRNNCell(keras.layers.Layer):
def __init__(self, units, **kwargs):
self.units = units
self.state_size = units
super(MinimalRNNCell, self).__init__(**kwargs)
def build(self, input_shape):
self.kernel = self.add_weight(shape=(input_shape[-1], self.units),
initializer='uniform',
name='kernel')
self.recurrent_kernel = self.add_weight(
shape=(self.units, self.units),
initializer='uniform',
name='recurrent_kernel')
self.built = True
def call(self, inputs, states):
prev_output = states[0]
h = K.dot(inputs, self.kernel)
output = h + K.dot(prev_output, self.recurrent_kernel)
return output, [output]
cells = [MinimalRNNCell(32), MinimalRNNCell(64)]
x = keras.Input((None, 257))
y = RNN(cells)(x)
out = Dense(257, name='fin_dense')(y)
out = Activation('sigmoid', name='out_layer')(out)
model = Model(inputs=x, outputs=out)
我可以使用得到预测,
in_test = np.random.randn(1, 3, 257)
mod_out = model.predict(in_test)
但是我想要每个图层的输出,当我尝试使用适用于所有其他Keras模型的getLayerOutputs
函数时>
def getLayerOutputs(model, input_data, learning_phase=1): outputs = [layer.output for layer in model.layers[1:]] # exclude Input layers_fn = K.function([model.input, K.learning_phase()], outputs) return layers_fn([input_data, learning_phase]) layer_outs = getLayerOutputs(model, in_test)
我获得整个RNN层的输出,而不是其中的每个单元的输出,如何使用RNN获得每个单元的输出?
我正在尝试获取Keras中自定义RNN模型每一层的输出。该模型的代码如下。从tensorflow.python导入keras从tensorflow.python.keras导入后端...
这应该在tensorflow 1.x(测试版本1.12.0)上执行:
通过像这样将return_state标志更改为True