tf rnn layer

流程

先介绍下数据和整体流程

生成数据

import numpy as nprows = 10000
columns = 100
emb_size = 5train_x = np.random.random(size=(rows, columns, emb_size))
train_y = np.random.randint(low=0, high=2, size=(rows, 1))

tf 计算流程

import tensorflow as tf
from tensorflow import kerasmodel = keras.Sequential()
model.add(keras.layers.Input(shape=[columns, emb_size], name='my_input')) # batch_size为None
model.add(keras.layers.SimpleRNN(units=10, use_bias=False, kernel_initializer=keras.initializers.Zeros()))
model.add(keras.layers.Dense(1, kernel_initializer=keras.initializers.Orthogonal(gain=1.0, seed=None)))model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),optimizer=tf.keras.optimizers.Adam(1e-4),metrics=['accuracy'])model.fit(train_x, train_y, epochs=10, batch_size=100)

tf 实现SimpleRNN

class MySimpleRNN(keras.layers.B):def __init__(self):super(MySimpleRNN, self).__init__()passdef build(self):passdef call(self):pass

numpy 计算流程

import numpy as np

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