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GRU_test_load_weight.py
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GRU_test_load_weight.py
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import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import GRU, Dense
import numpy as np
# 生成虚拟数据
def generate_data():
# 生成输入序列和对应的目标值
seq_length = 10
num_samples = 10
x = np.random.rand(num_samples, seq_length, 3) # 3维向量的输入序列
y = np.sum(x, axis=2) # 目标值是输入序列各元素之和
# 将y的每个元素转换为自身的列表
y_as_list = np.array([[[element] for element in row] for row in y])
return x, y_as_list
# 构建模型
def build_model():
model = Sequential()
model.add(GRU(50, input_shape=(None, 3), return_sequences=True)) # 使用50个GRU单元
model.add(Dense(1)) # 输出层,用于回归问题
return model
# 编译模型
model = build_model()
model.load_weights('gru_weights.h5')
# 生成数据
x_train, y_train = generate_data()
print("x_train: \n" + x_train)
print("y_train: \n" + y_train)
for a, b in zip(x_train[0], y_train[0]):
print(f"sum {a} = {b}")
# 预测并计算每个序列输出的和
predictions = model.predict(x_train)
# 打印结果
print("Predictions:")
print(predictions)
for i in range(10):
print(i, " -------")
for a, b, c in zip(x_train[-5 + i], y_train[-5 + i], predictions[-5+i]):
print(f"act_sum {a} = {b}, pre: {c}")