history = gru.fit(x_train, y_train, epochs=40, batch_size=16, validation_data=(x_test, y_test), verbose=1)含义
时间: 2023-03-16 15:44:26 浏览: 65
这表明我们正在使用GRU(递归神经网络)来训练模型,其中epochs=40表示我们进行40轮训练,batch_size=16表示我们每次使用16个样本进行训练,validation_data=(x_test, y_test)表示在每个epoch结束时使用x_test和y_test数据集来评估模型的性能,verbose=1表示我们想要看到更多的训练过程信息。
相关问题
import numpy as np import matplotlib.pyplot as plt import pickle as pkl import pandas as pd import tensorflow.keras from tensorflow.keras.models import Sequential, Model, load_model from tensorflow.keras.layers import LSTM, GRU, Dense, RepeatVector, TimeDistributed, Input, BatchNormalization, \ multiply, concatenate, Flatten, Activation, dot from sklearn.metrics import mean_squared_error,mean_absolute_error from tensorflow.keras.optimizers import Adam from tensorflow.python.keras.utils.vis_utils import plot_model from tensorflow.keras.callbacks import EarlyStopping from keras.callbacks import ReduceLROnPlateau df = pd.read_csv('lorenz.csv') signal = df['signal'].values.reshape(-1, 1) x_train_max = 128 signal_normalize = np.divide(signal, x_train_max) def truncate(x, train_len=100): in_, out_, lbl = [], [], [] for i in range(len(x) - train_len): in_.append(x[i:(i + train_len)].tolist()) out_.append(x[i + train_len]) lbl.append(i) return np.array(in_), np.array(out_), np.array(lbl) X_in, X_out, lbl = truncate(signal_normalize, train_len=50) X_input_train = X_in[np.where(lbl <= 9500)] X_output_train = X_out[np.where(lbl <= 9500)] X_input_test = X_in[np.where(lbl > 9500)] X_output_test = X_out[np.where(lbl > 9500)] # Load model model = load_model("model_forecasting_seq2seq_lstm_lorenz.h5") opt = Adam(lr=1e-5, clipnorm=1) model.compile(loss='mean_squared_error', optimizer=opt, metrics=['mae']) #plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True) # Train model early_stop = EarlyStopping(monitor='val_loss', patience=20, verbose=1, mode='min', restore_best_weights=True) #reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=9, verbose=1, mode='min', min_lr=1e-5) #history = model.fit(X_train, y_train, epochs=500, batch_size=128, validation_data=(X_test, y_test),callbacks=[early_stop]) #model.save("lstm_model_lorenz.h5") # 对测试集进行预测 train_pred = model.predict(X_input_train[:, :, :]) * x_train_max test_pred = model.predict(X_input_test[:, :, :]) * x_train_max train_true = X_output_train[:, :] * x_train_max test_true = X_output_test[:, :] * x_train_max # 计算预测指标 ith_timestep = 10 # Specify the number of recursive prediction steps # List to store the predicted steps pred_len =2 predicted_steps = [] for i in range(X_output_test.shape[0]-pred_len+1): YPred =[],temdata = X_input_test[i,:] for j in range(pred_len): Ypred.append (model.predict(temdata)) temdata = [X_input_test[i,j+1:-1],YPred] # Convert the predicted steps into numpy array predicted_steps = np.array(predicted_steps) # Plot the predicted steps #plt.plot(X_output_test[0:ith_timestep], label='True') plt.plot(predicted_steps, label='Predicted') plt.legend() plt.show()
这段代码看起来是一个用于时间序列预测的深度学习模型。该模型使用了序列到序列 LSTM (Seq2Seq LSTM) 模型进行预测,使用了 EarlyStopping 回调函数来避免过度拟合,并使用 Adam 优化器来进行模型优化。
具体来说,该代码读取了一个名为 'lorenz.csv' 的数据文件,将其中的信号列读取出来并进行了归一化处理。然后,使用 truncate 函数将信号序列切割成训练集和测试集,将其输入到 Seq2Seq LSTM 模型中进行训练。训练完成后,对测试集进行预测并计算预测指标,最后使用 matplotlib 库将预测结果可视化。
如果需要更详细的解释或修改建议,请提出具体问题或要求。
origin_input = Input(shape=(time_step, features)) # 模型输入 time_step*(N+1),N为分解所得分量数 cominput = origin_input[:, :, 1:] # 分解所得分量构成的序列 time_step*N output = concatenate( [Conv1D(kernel_size=3, filters=64, activation='relu', padding='same')(tf.expand_dims(cominput[:, :, ts], axis=-1)) for ts in range(features-1)], axis=-1) output = Dense(64, activation='relu')(output) # 拼接所得结果经全连接层进行降维&转换 res = Conv1D(kernel_size=1, filters=64, activation='relu')(tf.expand_dims(origin_input[:, :, 0], axis=-1)) output = concatenate((output, res), axis=-1) output = Bidirectional(GRU(64, return_sequences=True))(output) output = Bidirectional(GRU(64, return_sequences=True))(output) time_last = tf.transpose(output, [0, 2, 1]) att_1 = Dense(time_step, activation='tanh')(time_last) att_2 = Dense(time_step, activation='softmax', use_bias=False)(att_1) time_att = Multiply()([time_last, att_2]) out = tf.reduce_sum(time_att, axis=-1) output = Dense(1, activation='sigmoid')(out) model = Model(inputs=origin_input, outputs=output, name='proposed_model') opt = tf.keras.optimizers.Adam(learning_rate=0.001) model.compile(loss=losses.mse, optimizer=opt) model.summary() lr_reducer = ReduceLROnPlateau(factor=0.5, patience=5) callbacks = [lr_reducer] model.fit(x_train_scaled, y_train_scaled, epochs=100, batch_size=512, validation_split=0.1, callbacks=callbacks)
这是一个使用Keras框架搭建的神经网络模型,主要用于时序数据的预测。模型的输入是一个形状为(time_step, features)的张量,其中time_step表示时间步长,features表示每个时间步上的特征数。模型首先通过对输入的分解,得到分解所得分量构成的序列,然后对这些分量进行卷积操作,将卷积所得结果拼接在一起,经过全连接层降维后再和原始输入的第一个特征进行拼接,得到一个新的张量。接着,将这个张量输入到两层双向GRU中,得到一个形状为(time_step, 128)的张量。然后,将这个张量转置后输入到两个全连接层中,得到一个形状为(batch_size, time_step)的张量,表示对于每个时间步的不同权重。最后,将这个张量和上一步得到的(time_step, 128)的张量相乘,并对时间步这一维进行求和,得到一个形状为(batch_size, 1)的张量,表示对于整个时间序列的预测结果。模型的损失函数采用均方误差,优化器采用Adam。模型在训练过程中使用ReduceLROnPlateau回调函数进行学习率调整。