Recap

input dim, hidden dim

from tensorflow.keras import layers
# \(xw_{xh} + hw_{nn}\)，3次

cell = layers.SimpleRNNCell(3)

cell.build(input_shape=(None, 4))
cell.trainable_variables

[<tf.Variable ‘kernel:0‘ shape=(4, 3) dtype=float32, numpy=
 array([[-0.5311725 ,  0.7757399 , -0.19041312],
        [ 0.90420175, -0.14276218,  0.1546886 ],
        [ 0.81770146, -0.46731013, -0.05373603],
        [ 0.49086082,  0.10275221,  0.10146773]], dtype=float32)>,
 <tf.Variable ‘recurrent_kernel:0‘ shape=(3, 3) dtype=float32, numpy=
 array([[ 0.7557267 , -0.58395827,  0.2964283 ],
        [-0.64145935, -0.56886935,  0.5147014 ],
        [-0.13193521, -0.5791204 , -0.8044953 ]], dtype=float32)>,
 <tf.Variable ‘bias:0‘ shape=(3,) dtype=float32, numpy=array([0., 0., 0.], dtype=float32)>]

SimpleRNNCell

• out,h1=call(x,h0)$out,h_1=call(x,h_0)$

  • x: [b,seq len,word vec]

  • h0/h1:[b,hdim]$h_0/h_1:[b,hdim]$

  • out: [b,h dim]

Single layer RNN Cell

import tensorflow as tf
x = tf.random.normal([4, 80, 100])

ht0 = x[:, 0, :]
cell = tf.keras.layers.SimpleRNNCell(64)
out, ht1 = cell(ht0, [tf.zeros([4, 64])])
out.shape, ht1[0].shape

[]





(TensorShape([4, 64]), TensorShape([4, 64]))

id(out), id(ht1[0])  # same id

(4877125168, 4877125168)

Multi-Layers RNN

x = tf.random.normal([4, 80, 100])
ht0 = x[:, 0, :]
cell = tf.keras.layers.SimpleRNNCell(64)

cell2 = tf.keras.layers.SimpleRNNCell(64)

state0 = [tf.zeros([4, 64])]

state1 = [tf.zeros([4, 64])]
out0, state0 = cell(ht0, state0)

out2, state2 = cell2(out, state2)
out2.shape, state2[0].shape

(TensorShape([4, 64]), TensorShape([4, 64]))

RNN Layer

self.run = keras.Sequential([
    layers.SimpleRNN(units,dropout=0.5,return_sequences=Ture,unroll=True),
    layers.SimpleRNN(units,dropout=0.5,unroll=True)
])
x=self.rnn(x)