TimeDistributed(Dense) vs Dense in Keras - Same number of parameters

Question

I\'m building a model that converts a string to another string using recurrent layers (GRUs). I have tried both a Dense and a TimeDistributed(Dense) layer as the last-but-on

Answer 1

Here is a piece of code that verifies TimeDistirbuted(Dense(X)) is identical to Dense(X):

import numpy as np 
from keras.layers import Dense, TimeDistributed
import tensorflow as tf

X = np.array([ [[1, 2, 3],
                [4, 5, 6],
                [7, 8, 9],
                [10, 11, 12]
               ],
               [[3, 1, 7],
                [8, 2, 5],
                [11, 10, 4],
                [9, 6, 12]
               ]
              ]).astype(np.float32)
print(X.shape)

(2, 4, 3)

dense_weights = np.array([[0.1, 0.2, 0.3, 0.4, 0.5],
                          [0.2, 0.7, 0.9, 0.1, 0.2],
                          [0.1, 0.8, 0.6, 0.2, 0.4]])
bias = np.array([0.1, 0.3, 0.7, 0.8, 0.4])
print(dense_weights.shape)

(3, 5)

dense = Dense(input_dim=3, units=5, weights=[dense_weights, bias])
input_tensor = tf.Variable(X, name='inputX')
output_tensor1 = dense(input_tensor)
output_tensor2 = TimeDistributed(dense)(input_tensor)
print(output_tensor1.shape)
print(output_tensor2.shape)

(2, 4, 5)

(2, ?, 5)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    output1 = sess.run(output_tensor1)
    output2 = sess.run(output_tensor2)

print(output1 - output2)

And the difference is:

[[[0. 0. 0. 0. 0.]
  [0. 0. 0. 0. 0.]
  [0. 0. 0. 0. 0.]
  [0. 0. 0. 0. 0.]]

 [[0. 0. 0. 0. 0.]
  [0. 0. 0. 0. 0.]
  [0. 0. 0. 0. 0.]
  [0. 0. 0. 0. 0.]]]