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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])
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WYXOGO
