循环计算过程（4pre1）

 4pre1

 

#! /usr/bin/env python
# -*- coding:utf-8 -*-
# __author__ = "y00475869"


import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Dense, SimpleRNN
import matplotlib.pyplot as plt
import os

input_word = "abcde"
w_to_id = {'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4}  # 单词映射到数值id的词典
id_to_onehot = {0: [1., 0., 0., 0., 0.], 1: [0., 1., 0., 0., 0.], 2: [0., 0., 1., 0., 0.], 3: [0., 0., 0., 1., 0.],
                4: [0., 0., 0., 0., 1.]}  # id编码为one-hot

x_train = [
    [id_to_onehot[w_to_id['a']], id_to_onehot[w_to_id['b']], id_to_onehot[w_to_id['c']], id_to_onehot[w_to_id['d']]],
    [id_to_onehot[w_to_id['b']], id_to_onehot[w_to_id['c']], id_to_onehot[w_to_id['d']], id_to_onehot[w_to_id['e']]],
    [id_to_onehot[w_to_id['c']], id_to_onehot[w_to_id['d']], id_to_onehot[w_to_id['e']], id_to_onehot[w_to_id['a']]],
    [id_to_onehot[w_to_id['d']], id_to_onehot[w_to_id['e']], id_to_onehot[w_to_id['a']], id_to_onehot[w_to_id['b']]],
    [id_to_onehot[w_to_id['e']], id_to_onehot[w_to_id['a']], id_to_onehot[w_to_id['b']], id_to_onehot[w_to_id['c']]],
]
y_train = [w_to_id['e'], w_to_id['a'], w_to_id['b'], w_to_id['c'], w_to_id['d']]

np.random.seed(7)
np.random.shuffle(x_train)
np.random.seed(7)
np.random.shuffle(y_train)
tf.random.set_seed(7)

# 使x_train符合SimpleRNN输入要求：[送入样本数， 循环核时间展开步数， 每个时间步输入特征个数]。
# 此处整个数据集送入，送入样本数为len(x_train)；输入4个字母出结果，循环核时间展开步数为4; 表示为独热码有5个输入特征，每个时间步输入特征个数为5
x_train = np.reshape(x_train, (len(x_train), 4, 5))
y_train = np.array(y_train)

model = tf.keras.Sequential([
    SimpleRNN(3),
    Dense(5, activation='softmax')
])

model.compile(optimizer=tf.keras.optimizers.Adam(0.01),
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
              metrics=['sparse_categorical_accuracy'])

checkpoint_save_path = "./checkpoint/rnn_onehot_4pre1.ckpt"

if os.path.exists(checkpoint_save_path + '.index'):
    print('-------------load the model-----------------')
    model.load_weights(checkpoint_save_path)

cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,
                                                 save_weights_only=True,
                                                 save_best_only=True,
                                                 monitor='loss')  # 由于fit没有给出测试集，不计算测试集准确率，根据loss，保存最优模型

history = model.fit(x_train, y_train, batch_size=32, epochs=100, callbacks=[cp_callback])

model.summary()

# print(model.trainable_variables)
file = open('./weights.txt', 'w')  # 参数提取
for v in model.trainable_variables:
    file.write(str(v.name) + '\n')
    file.write(str(v.shape) + '\n')
    file.write(str(v.numpy()) + '\n')
file.close()

###############################################    show   ###############################################

# 显示训练集和验证集的acc和loss曲线
acc = history.history['sparse_categorical_accuracy']
loss = history.history['loss']

plt.subplot(1, 2, 1)
plt.plot(acc, label='Training Accuracy')
plt.title('Training Accuracy')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(loss, label='Training Loss')
plt.title('Training Loss')
plt.legend()
plt.show()

############### predict #############

preNum = int(input("input the number of test alphabet:"))
for i in range(preNum):
    alphabet1 = input("input test alphabet:")
    alphabet = [id_to_onehot[w_to_id[a]] for a in alphabet1]
    # 使alphabet符合SimpleRNN输入要求：[送入样本数， 循环核时间展开步数， 每个时间步输入特征个数]。
    # 此处验证效果送入了1个样本，送入样本数为1；输入4个字母出结果，所以循环核时间展开步数为4; 表示为独热码有5个输入特征，每个时间步输入特征个数为5
    alphabet = np.reshape(alphabet, (1, 4, 5))
    result = model.predict([alphabet])
    pred = tf.argmax(result, axis=1)
    pred = int(pred)
    tf.print(alphabet1 + '->' + input_word[pred])

输出结果：