print(X_train) plt.plot(x, X_train.T) plt.show() X_train = np.array(X_train)

As an AI language model, I cannot provide the value for X_train without additional information. However, the code above converts the X_train variable into a numpy array and then plots the transpose of the X_train values using matplotlib. The resulting plot will show the distribution of the X_train values.

相关问题

import pandas as pd import numpy as np from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import Dense, LSTM import matplotlib.pyplot as plt # 读取CSV文件 data = pd.read_csv('77.csv', header=None) # 将数据集划分为训练集和测试集 train_size = int(len(data) * 0.7) train_data = data.iloc[:train_size, 1:2].values.reshape(-1,1) test_data = data.iloc[train_size:, 1:2].values.reshape(-1,1) # 对数据进行归一化处理 scaler = MinMaxScaler(feature_range=(0, 1)) train_data = scaler.fit_transform(train_data) test_data = scaler.transform(test_data) # 构建训练集和测试集 def create_dataset(dataset, look_back=1): X, Y = [], [] for i in range(len(dataset) - look_back): X.append(dataset[i:(i+look_back), 0]) Y.append(dataset[i+look_back, 0]) return np.array(X), np.array(Y) look_back = 3 X_train, Y_train = create_dataset(train_data, look_back) X_test, Y_test = create_dataset(test_data, look_back) # 转换为LSTM所需的输入格式 X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) # 构建LSTM模型 model = Sequential() model.add(LSTM(units=50, return_sequences=True, input_shape=(look_back, 1))) model.add(LSTM(units=50)) model.add(Dense(units=1)) model.compile(optimizer='adam', loss='mean_squared_error') model.fit(X_train, Y_train, epochs=100, batch_size=32) # 预测测试集并进行反归一化处理 Y_pred = model.predict(X_test) Y_pred = scaler.inverse_transform(Y_pred) Y_test = scaler.inverse_transform(Y_test) # 输出RMSE指标 rmse = np.sqrt(np.mean((Y_pred - Y_test)**2)) print('RMSE:', rmse) # 绘制训练集真实值和预测值图表 train_predict = model.predict(X_train) train_predict = scaler.inverse_transform(train_predict) train_actual = scaler.inverse_transform(Y_train.reshape(-1, 1)) plt.plot(train_actual, label='Actual') plt.plot(train_predict, label='Predicted') plt.title('Training Set') plt.xlabel('Time (h)') plt.ylabel('kWh') plt.legend() plt.show() # 绘制测试集真实值和预测值图表 plt.plot(Y_test, label='Actual') plt.plot(Y_pred, label='Predicted') plt.title('Testing Set') plt.xlabel('Time (h)') plt.ylabel('kWh') plt.legend() plt.show()以上代码运行时报错，错误为ValueError: Expected 2D array, got 1D array instead: array=[-0.04967795 0.09031832 0.07590125]. Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.如何进行修改

可以将以下代码：

X_train, Y_train = create_dataset(train_data, look_back)
X_test, Y_test = create_dataset(test_data, look_back)

X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))

修改为：

X_train, Y_train = create_dataset(train_data, look_back)
X_test, Y_test = create_dataset(test_data, look_back)

X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))

Y_train = np.reshape(Y_train, (Y_train.shape[0], 1))
Y_test = np.reshape(Y_test, (Y_test.shape[0], 1))

这样就可以解决报错问题。

tokenizer = Tokenizer(num_words=max_words) tokenizer.fit_on_texts(data['text']) sequences = tokenizer.texts_to_sequences(data['text']) word_index = tokenizer.word_index print('Found %s unique tokens.' % len(word_index)) data = pad_sequences(sequences,maxlen=maxlen) labels = np.array(data[:,:1]) print('Shape of data tensor:',data.shape) print('Shape of label tensor',labels.shape) indices = np.arange(data.shape[0]) np.random.shuffle(indices) data = data[indices] labels = labels[indices] x_train = data[:traing_samples] y_train = data[:traing_samples] x_val = data[traing_samples:traing_samples+validation_samples] y_val = data[traing_samples:traing_samples+validation_samples] model = Sequential() model.add(Embedding(max_words,100,input_length=maxlen)) model.add(Flatten()) model.add(Dense(32,activation='relu')) model.add(Dense(10000,activation='sigmoid')) model.summary() model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc']) history = model.fit(x_train,y_train, epochs=1, batch_size=128, validation_data=[x_val,y_val]) import matplotlib.pyplot as plt acc = history.history['acc'] val_acc = history.history['val_acc'] loss = history.history['loss'] val_loss = history.history['val_loss'] epoachs = range(1,len(acc) + 1) plt.plot(epoachs,acc,'bo',label='Training acc') plt.plot(epoachs,val_acc,'b',label = 'Validation acc') plt.title('Training and validation accuracy') plt.legend() plt.figure() plt.plot(epoachs,loss,'bo',label='Training loss') plt.plot(epoachs,val_loss,'b',label = 'Validation loss') plt.title('Training and validation loss') plt.legend() plt.show() max_len = 10000 x_train = keras.preprocessing.sequence.pad_sequences(x_train, maxlen=max_len) x_test = data[10000:,0:] x_test = keras.preprocessing.sequence.pad_sequences(x_test, maxlen=max_len) # 将标签转换为独热编码 y_train = np.eye(2)[y_train] y_test = data[10000:,:1] y_test = np.eye(2)[y_test]

这段代码中存在几个问题：

1. `labels` 的赋值有误。应该是 `labels = np.array(data[:, 1:])`，因为标签是从第二列开始的。

2. `validation_data` 的格式有误。应该是 `validation_data=(x_val, y_val)`，而不是 `validation_data=[x_val, y_val]`。

3. 在使用 `Sequential` 模型时，需要指定输入的维度。这里应该是 `model.add(Dense(10000, activation='sigmoid', input_dim=maxlen))`，而不是 `model.add(Dense(10000, activation='sigmoid'))`。

4. 绘制图表时，应该使用 `val_acc` 和 `val_loss`，而不是 `test_acc` 和 `test_loss`。

5. 在将标签转换为独热编码时，应该使用 `y_train[:, 0]` 和 `y_test[:, 0]`，而不是 `y_train` 和 `y_test`。

以下是修改后的代码：

tokenizer = Tokenizer(num_words=max_words)
tokenizer.fit_on_texts(data['text'])
sequences = tokenizer.texts_to_sequences(data['text'])
word_index = tokenizer.word_index
print('Found %s unique tokens.' % len(word_index))

data = pad_sequences(sequences, maxlen=maxlen)
labels = np.array(data[:, 1:])
print('Shape of data tensor:', data.shape)
print('Shape of label tensor', labels.shape)

indices = np.arange(data.shape[0])
np.random.shuffle(indices)
data = data[indices]
labels = labels[indices]

x_train = data[:training_samples]
y_train = labels[:training_samples]
x_val = data[training_samples:training_samples+validation_samples]
y_val = labels[training_samples:training_samples+validation_samples]

model = Sequential()
model.add(Embedding(max_words, 100, input_length=maxlen))
model.add(Flatten())
model.add(Dense(32, activation='relu'))
model.add(Dense(10000, activation='sigmoid', input_dim=maxlen))
model.summary()

model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['acc'])

history = model.fit(x_train, y_train,
                    epochs=1,
                    batch_size=128,
                    validation_data=(x_val, y_val))

import matplotlib.pyplot as plt

acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(1, len(acc) + 1)

plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()

plt.figure()

plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()

plt.show()

max_len = 10000
x_train = keras.preprocessing.sequence.pad_sequences(x_train, maxlen=max_len)
x_test = data[10000:, 0:]
x_test = keras.preprocessing.sequence.pad_sequences(x_test, maxlen=max_len)

# 将标签转换为独热编码
y_train = np.eye(2)[y_train[:, 0]]
y_test = data[10000:, 1:]
y_test = np.eye(2)[y_test[:, 0]]