import pandas as pd import numpy as np import matplotlib.pyplot as plt from keras.models import Model, Input from keras.layers import Conv1D, BatchNormalization, Activation, Add, Flatten, Dense from keras.optimizers import Adam # 读取CSV文件 data = pd.read_csv("3c_left_1-6.csv", header=None) # 将数据转换为Numpy数组 data = data.values # 定义输入形状 input_shape = (data.shape[1], 1) # 定义深度残差网络 def residual_network(inputs): # 第一层卷积层 x = Conv1D(32, 3, padding="same")(inputs) x = BatchNormalization()(x) x = Activation("relu")(x) # 残差块 for i in range(5): y = Conv1D(32, 3, padding="same")(x) y = BatchNormalization()(y) y = Activation("relu")(y) y = Conv1D(32, 3, padding="same")(y) y = BatchNormalization()(y) y = Add()([x, y]) x = Activation("relu")(y) # 全局池化层和全连接层 x = Flatten()(x) x = Dense(128, activation="relu")(x) x = Dense(data.shape[1], activation="linear")(x) outputs = Add()([x, inputs]) return outputs # 构建模型 inputs = Input(shape=input_shape) outputs = residual_network(inputs) model = Model(inputs=inputs, outputs=outputs) # 编译模型 model.compile(loss="mean_squared_error", optimizer=Adam()) # 训练模型 model.fit(data[..., np.newaxis], data[..., np.newaxis], epochs=100) # 预测数据 predicted_data = model.predict(data[..., np.newaxis]) predicted_data = np.squeeze(predicted_data) # 可视化去噪前后的数据 fig, axs = plt.subplots(3, 1, figsize=(12, 8)) for i in range(3): axs[i].plot(data[:, i], label="Original Signal") axs[i].plot(predicted_data[:, i], label="Denoised Signal") axs[i].legend() plt.savefig("denoised_signal.png") # 将去噪后的数据保存为CSV文件 df = pd.DataFrame(predicted_data, columns=["x", "y", "z"]) df.to_csv("denoised_data.csv", index=False)报错为Traceback (most recent call last): File "G:\project2\main.py", line 51, in <module> model.fit(data[..., np.newaxis], data[..., np.newaxis], epochs=100) File "G:\python\envs\tensorflow\lib\site-packages\keras\engine\training.py", line 1154, in fit batch_size=batch_size) File "G:\python\envs\tensorflow\lib\site-packages\keras\engine\training.py", line 621, in _standardize_user_data exception_prefix='target') File "G:\python\envs\tensorflow\lib\site-packages\keras\engine\training_utils.py", line 145, in standardize_input_data str(data_shape)) ValueError: Error when checking target: expected add_6 to have shape (3, 3) but got array with shape (3, 1)

这个错误是因为模型的输出形状与训练数据的形状不匹配。根据代码，模型的输出形状为 `(None, 3, 1)`，其中 `None` 表示输入数据的样本数可以是任意值，而训练数据的形状为 `(n_samples, 3)`，没有考虑通道数。因此需要将训练数据的形状转换为 `(n_samples, 3, 1)`，可以使用 `np.newaxis` 在最后一个维度添加一个通道维度，例如：

model.fit(data[..., np.newaxis], data[..., np.newaxis], epochs=100)

在预测数据时，也需要将输入数据的形状转换为 `(n_samples, 3, 1)`，例如：

predicted_data = model.predict(data[..., np.newaxis])

这样就可以解决这个错误了。

相关问题

import pandas as pd import numpy as np import matplotlib.pyplot as plt from keras.models import Model, Input from keras.layers import Conv1D, BatchNormalization, Activation, Add, Flatten, Dense from keras.optimizers import Adam # 读取CSV文件 data = pd.read_csv("3c_left_1-6.csv", header=None) # 将数据转换为Numpy数组 data = data.values # 定义输入形状 input_shape = (data.shape[1], 1) # 定义深度残差网络 def residual_network(inputs): # 第一层卷积层 x = Conv1D(32, 3, padding="same")(inputs) x = BatchNormalization()(x) x = Activation("relu")(x) # 残差块 for i in range(5): y = Conv1D(32, 3, padding="same")(x) y = BatchNormalization()(y) y = Activation("relu")(y) y = Conv1D(32, 3, padding="same")(y) y = BatchNormalization()(y) y = Add()([x, y]) x = Activation("relu")(y) # 全局池化层和全连接层 x = Flatten()(x) x = Dense(128, activation="relu")(x) x = Dense(data.shape[1], activation="linear")(x) outputs = Add()([x, inputs]) return outputs # 构建模型 inputs = Input(shape=input_shape) outputs = residual_network(inputs) model = Model(inputs=inputs, outputs=outputs) # 编译模型 model.compile(loss="mean_squared_error", optimizer=Adam()) # 训练模型 model.fit(data[..., np.newaxis], data[..., np.newaxis], epochs=100) # 预测数据 predicted_data = model.predict(data[..., np.newaxis]) # 可视化去噪前后的数据 fig, axs = plt.subplots(3, 1, figsize=(12, 8)) for i in range(3): axs[i].plot(data[:, i], label="Original Signal") axs[i].plot(predicted_data[:, i], label="Denoised Signal") axs[i].legend() plt.savefig("denoised_signal.png") # 将去噪后的数据保存为CSV文件 df = pd.DataFrame(predicted_data, columns=["x", "y", "z"]) df.to_csv("denoised_data.csv", index=False)报错为

您好，这段代码存在一些缩进错误，需要将import语句和定义residual_network函数的代码块缩进一下，如下所示：

import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 
from keras.models import Model, Input 
from keras.layers import Conv1D, BatchNormalization, Activation, Add, Flatten, Dense 
from keras.optimizers import Adam 

# 读取CSV文件 
data = pd.read_csv("3c_left_1-6.csv", header=None) 

# 将数据转换为Numpy数组 
data = data.values 

# 定义输入形状 
input_shape = (data.shape[1], 1) 

# 定义深度残差网络 
def residual_network(inputs): 
    # 第一层卷积层 
    x = Conv1D(32, 3, padding="same")(inputs) 
    x = BatchNormalization()(x) 
    x = Activation("relu")(x) 

    # 残差块 
    for i in range(5): 
        y = Conv1D(32, 3, padding="same")(x) 
        y = BatchNormalization()(y) 
        y = Activation("relu")(y) 
        y = Conv1D(32, 3, padding="same")(y) 
        y = BatchNormalization()(y) 
        y = Add()([x, y]) 
        x = Activation("relu")(y) 

    # 全局池化层和全连接层 
    x = Flatten()(x) 
    x = Dense(128, activation="relu")(x) 
    x = Dense(data.shape[1], activation="linear")(x) 
    outputs = Add()([x, inputs]) 

    return outputs 

# 构建模型 
inputs = Input(shape=input_shape) 
outputs = residual_network(inputs) 
model = Model(inputs=inputs, outputs=outputs) 

# 编译模型 
model.compile(loss="mean_squared_error", optimizer=Adam()) 

# 训练模型 
model.fit(data[..., np.newaxis], data[..., np.newaxis], epochs=100) 

# 预测数据 
predicted_data = model.predict(data[..., np.newaxis]) 

# 可视化去噪前后的数据 
fig, axs = plt.subplots(3, 1, figsize=(12, 8)) 
for i in range(3): 
    axs[i].plot(data[:, i], label="Original Signal") 
    axs[i].plot(predicted_data[:, i], label="Denoised Signal") 
    axs[i].legend() 
plt.savefig("denoised_signal.png") 

# 将去噪后的数据保存为CSV文件 
df = pd.DataFrame(predicted_data, columns=["x", "y", "z"]) 
df.to_csv("denoised_data.csv", index=False)

另外，如果您在运行代码时仍然遇到错误，请提供完整的错误信息，以便更好地帮助您解决问题。

import matplotlib.pyplot as plt import pandas as pd from keras.models import Sequential from keras import layers from keras import regularizers import os import keras import keras.backend as K import numpy as np from keras.callbacks import LearningRateScheduler data = "data.csv" df = pd.read_csv(data, header=0, index_col=0) df1 = df.drop(["y"], axis=1) lbls = df["y"].values - 1 wave = np.zeros((11500, 178)) z = 0 for index, row in df1.iterrows(): wave[z, :] = row z+=1 mean = wave.mean(axis=0) wave -= mean std = wave.std(axis=0) wave /= std def one_hot(y): lbl = np.zeros(5) lbl[y] = 1 return lbl target = [] for value in lbls: target.append(one_hot(value)) target = np.array(target) wave = np.expand_dims(wave, axis=-1) model = Sequential() model.add(layers.Conv1D(64, 15, strides=2, input_shape=(178, 1), use_bias=False)) model.add(layers.ReLU()) model.add(layers.Conv1D(64, 3)) model.add(layers.Conv1D(64, 3, strides=2)) model.add(layers.BatchNormalization()) model.add(layers.Dropout(0.5)) model.add(layers.Conv1D(64, 3)) model.add(layers.Conv1D(64, 3, strides=2)) model.add(layers.BatchNormalization()) model.add(layers.LSTM(64, dropout=0.5, return_sequences=True)) model.add(layers.LSTM(64, dropout=0.5, return_sequences=True)) model.add(layers.LSTM(32)) model.add(layers.Dropout(0.5)) model.add(layers.Dense(5, activation="softmax")) model.summary() save_path = './keras_model3.h5' if os.path.isfile(save_path): model.load_weights(save_path) print('reloaded.') adam = keras.optimizers.adam() model.compile(optimizer=adam, loss="categorical_crossentropy", metrics=["acc"]) # 计算学习率 def lr_scheduler(epoch): # 每隔100个epoch，学习率减小为原来的0.5 if epoch % 100 == 0 and epoch != 0: lr = K.get_value(model.optimizer.lr) K.set_value(model.optimizer.lr, lr * 0.5) print("lr changed to {}".format(lr * 0.5)) return K.get_value(model.optimizer.lr) lrate = LearningRateScheduler(lr_scheduler) history = model.fit(wave, target, epochs=400, batch_size=128, validation_split=0.2, verbose=2, callbacks=[lrate]) model.save_weights(save_path) print(history.history.keys()) # summarize history for accuracy plt.plot(history.history['acc']) plt.plot(history.history['val_acc']) plt.title('model accuracy') plt.ylabel('accuracy') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.show() # summarize history for loss plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.show()

这段代码是用来训练一个深度学习模型的代码，使用了Keras和Tensorflow进行模型的搭建、训练和保存。该模型包含了卷积层、批量归一化层、Dropout层、LSTM层和全连接层，并且最终使用softmax激活函数得到预测的类别。通过对数据进行处理，将输入数据按照一定的规范进行规范化，将标签转换成了一个one-hot编码的形式。最后，该代码通过在数据上进行训练，得到一个可以用来做预测的深度学习模型。