如何使用Keras API在TensorFlow Eager中将压差应用于RNN的输出？

Question

I would like to apply dropout to the outputs from an RNN. For example, in Tensorflow 1.8.0, I could do this:

import tensorflow as tf
import tensorflow.contrib.eager as tfe

tfe.enable_eager_execution()

x = tf.random_uniform((10, 5, 3))

gru_cell1 = tf.contrib.rnn.GRUCell(2)
gru_cell1 = tf.contrib.rnn.DropoutWrapper(gru_cell1, output_keep_prob=0.5)
cell = tf.contrib.rnn.MultiRNNCell([gru_cell1])
init_state = cell.zero_state(10, tf.float32)

cell_output, _ = tf.nn.dynamic_rnn(cell, x,
                                   initial_state=init_state, time_major=False)
cell_output

How can I achieve the same thing using the Keras API?

I have thought of the following two ways but they were unsuccessful:

import tensorflow as tf
import tensorflow.contrib.eager as tfe

tfe.enable_eager_execution()

# Attempt 1
x = tf.random_uniform((10, 5, 3))

gru_layer = tf.keras.layers.GRU(2, return_sequences=True, input_shape=(10, 5, 3))
gru_layer = tf.keras.layers.Dropout(0.5)(gru_layer)

# Gives the following error:
# ValueError: Attempt to convert a value (<tensorflow.python.keras._impl.keras.layers.recurrent.GRU object
#  at 0x000001C520681F60>) with an unsupported type (<class 'tensorflow.python.keras._impl.keras.layers.recurrent.GRU'>) 
# to a Tensor.

# Attempt 2
x = tf.random_uniform((10, 5, 3))

gru_layer = tf.keras.layers.GRU(2, return_sequences=True, input_shape=(10, 5, 3))
gru_layer = tf.keras.layers.TimeDistributed(tf.keras.layers.Dropout(0.4))(gru_layer)

# Gives the following error:
# ValueError: as_list() is not defined on an unknown TensorShape.

Answer 1

To get the model output, without training, like you're doing in the TF code, the following code should work. Indeed, you need an Input layer, and to hook each layer to the previous one, and a Model as well:

import numpy as np
from keras.models import Model
from keras.layers import Dropout, GRU, Input

x = np.random.randn(10, 5, 3)

inputs = Input(shape=(5, 3))
gru_layer = GRU(2, return_sequences=True)(inputs)
gru_layer = Dropout(0.5)(gru_layer)

model = Model(inputs=inputs, outputs=gru_layer)

output = model.predict(x)