import matplotlib.pyplot as plt import tensorflow as tf from tensorflow import keras import numpy as np #加载IMDB数据 imdb = keras.datasets.imdb (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=100) print("训练记录数量:{},标签数量:{}".format(len(train_data),len(train_labels))) print(train_data[0]) #数据标准化 train_data = keras.preprocessing.sequence.pad_sequences(train_data,value=0,padding='post',maxlen=256) #text_data = keras.preprocessing.sequence.pad_sequences(train_data,value=0,padding='post',maxlen=256) text_data = keras.preprocessing.sequence.pad_sequences(test_data,value=0,padding='post',maxlen=256) print(train_data[0]) #构建模型 vocab_size = 10000 model = tf.keras.Sequential([tf.keras.layers.Embedding(vocab_size, 64), tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)), tf.keras.layers.Dense(64,activation='relu'), tf.keras.layers.Dense(1) ]) model.summary() #配置并训练模型 model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy']) x_val = train_data[:10000] partial_x_train = train_data[10000:] y_val = train_labels[:10000] partial_y_train = train_labels[10000:] history = model.fit(partial_x_train,partial_y_train,epochs=1,batch_size=512,validation_data=(x_val,y_val),verbose=1) #测试性能 results = model.evaluate(test_data, test_labels, verbose=2) print(results) #训练过程可视化 history_dict = history.history print(history_dict.keys()) def plot_graphs(history, string): plt.plot(history.history[string]) plt.plot(history.history['val_'+string]) plt.xlabel("Epochs") plt.ylabel(string) plt.legend([string,'val_'+string]) plt.show() plot_graphs(history,"accuracy") plot_graphs(history,"loss")
时间: 2024-03-14 20:49:03 浏览: 23
这段代码使用了IMDB电影评论数据集,通过对评论进行情感分析,将评论分为正面或负面两类。其中,数据标准化使用了keras.preprocessing.sequence.pad_sequences函数,将每条评论的长度都设置为256,模型使用了嵌入层、双向LSTM层和2个全连接层,其中嵌入层的大小为10000,LSTM层的大小为64,全连接层的大小为64和1。模型的优化器使用了Adam,损失函数使用了二元交叉熵,评价指标使用了准确率。并且,使用了matplotlib库,对模型的训练过程进行可视化。
相关问题
import matplotlib.pyplot as plt import tensorflow as tf from tensorflow import keras import numpy as np #加载IMDB数据 imdb = keras.datasets.imdb (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=100) print("训练记录数量:{},标签数量:{}".format(len(train_data),len(train_labels))) print(train_data[0]) #数据标准化 train_data = keras.preprocessing.sequence.pad_sequences(train_data,value=0,padding='post',maxlen=256) text_data = keras.preprocessing.sequence.pad_sequences(train_data,value=0,padding='post',maxlen=256) print(train_data[0]) #构建模型 vocab_size = 10000 model = tf.keras.Sequential([tf.keras.layers.Embedding(vocab_size, 64), tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)), tf.keras.layers.Dense(64,activation='relu'), tf.keras.layers.Dense(1) ]) model.summary() #配置并训练模型 model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy']) x_val = train_data[:10000] partial_x_train = train_data[10000:] y_val = train_labels[:10000] partial_y_train = train_labels[10000:] history = model.fit(partial_x_train,partial_y_train,epochs=1,batch_size=512,validation_data=(x_val,y_val),verbose=1) #测试性能 results = model.evaluate(test_data, test_labels, verbose=2) print(results) #训练过程可视化 history_dict = history.history print(history_dict.keys()) def plot_graphs(history, string): plt.plot(history.history[string]) plt.plot(history.history['val_'+string]) plt.xlabel("Epochs") plt.ylabel(string) plt.legend([string,'val_'+string]) plt.show() plot_graphs(history,"accuracy") plot_graphs(history,"loss")
这段代码是一个使用IMDB数据集进行情感分析的模型。该模型使用了嵌入层、双向LSTM层和两个全连接层。以下是这段代码的功能和步骤:
1. 加载 IMDB 数据集,包括训练数据和测试数据。
2. 对训练数据进行标准化,使用 `keras.preprocessing.sequence.pad_sequences` 来将序列填充或截断到相同的长度。
3. 构建模型,包括一个嵌入层、一个双向LSTM层和两个全连接层。
4. 配置并训练模型,使用 `model.compile` 来配置模型的优化器、损失函数和评价指标,然后使用 `model.fit` 来训练模型。
5. 测试模型性能,使用 `model.evaluate` 来评估模型在测试数据上的性能。
6. 可视化训练过程,使用 `plot_graphs` 函数来绘制模型在训练和验证集上的损失和准确率。
需要注意的是,这段代码中 `text_data` 的赋值是错误的,应该是 `text_data = keras.preprocessing.sequence.pad_sequences(test_data,value=0,padding='post',maxlen=256)`。同时,代码中只训练了一次,因为 `epochs=1`。如果想要更好的模型性能,应该增加训练次数。
import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import pandas as pd from sklearn.utils import shuffle from sklearn.preprocessing import scale df = pd.read_csv("C:\\boston.csv",header=0) ds = df.values from sklearn.datasets import load_boston boston = load_boston() X = boston.data y = boston.target from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense model = Sequential([ Dense(64, activation='relu', input_shape=(X_train.shape[1],)), Dense(64, activation='relu'), Dense(64, activation='relu'), Dense(1) ]) model.compile(loss='mean_squared_error', optimizer='adam') history = model.fit(X_train, y_train, validation_split=0.1, epochs=100, batch_size=32) from sklearn.metrics import mean_squared_error y_pred = model.predict(x_test)mse = mean_squared_error(y_test, y_pred)print('MSE:’, mse) import matplotlib.pyplot as plt plt.plot(history.history['accuracy'], label='train') plt.plot(history.history['val_accuracy'], label='validation') plt.legend() plt.show()
这段代码使用TensorFlow和Keras实现了一个神经网络模型,用于预测波士顿房价。以下是代码的解释:
首先,导入必要的库和模块:
```
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.utils import shuffle
from sklearn.preprocessing import scale
```
然后,读取波士顿房价数据集并对其进行预处理:
```
df = pd.read_csv("C:\\boston.csv",header=0)
ds = df.values
```
接着,从sklearn.datasets模块中加载波士顿房价数据集,并将其分为训练集和测试集:
```
from sklearn.datasets import load_boston
boston = load_boston()
X = boston.data
y = boston.target
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
对训练集和测试集进行标准化处理:
```
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
```
定义一个包含4个Dense层的神经网络模型:
```
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
model = Sequential([
Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
Dense(64, activation='relu'),
Dense(64, activation='relu'),
Dense(1)
])
```
编译模型并训练:
```
model.compile(loss='mean_squared_error', optimizer='adam')
history = model.fit(X_train, y_train, validation_split=0.1, epochs=100, batch_size=32)
```
使用模型对测试集进行预测,并计算均方误差:
```
from sklearn.metrics import mean_squared_error
y_pred = model.predict(x_test)
mse = mean_squared_error(y_test, y_pred)
print('MSE:’, mse)
```
最后,绘制模型的训练和验证准确率曲线:
```
import matplotlib.pyplot as plt
plt.plot(history.history['accuracy'], label='train')
plt.plot(history.history['val_accuracy'], label='validation')
plt.legend()
plt.show()
```
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