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流程

先介绍下数据和整体流程

生成数据

import numpy as nprows = 10000columns = 100emb_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 tffrom tensorflow import kerasmodel = keras.Sequential()model.add(keras.layers.Input(shape=[columns, emb_size], name='my_input')) # batch_size为Nonemodel.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__()		pass	def build(self):		pass	def call(self):		pass

numpy 计算流程

import numpy as np

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