from keras.datasets import imdb

(train_data,train_labels),(test_data,test_labels)=imdb.load_data(path=r"C:\Users\DELL\PycharmProjects\deeplearning\imdb.npz",num_words=10000)


import numpy as np
def vectorize_sequences(sequences, dimension=10000):
    results = np.zeros((len(sequences), dimension))
    for i, sequence in enumerate(sequences):
        results[i, sequence] = 1.
    return results


x_train = vectorize_sequences(train_data)
x_test = vectorize_sequences(test_data)

y_train=np.asarray(train_labels).astype('float32')
y_test=np.asarray(test_labels).astype('float32')

from keras import models
from keras import layers

model = models.Sequential()
model.add(layers.Dense(16,activation='relu',input_shape=(10000,)))
model.add(layers.Dense(16,activation='relu'))
model.add(layers.Dense(1,activation='sigmoid'))

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

#留出验证集，原始训练数据留出10000个样本作为验证集
x_val=x_train[:10000]
partial_x_train=x_train[10000:]
y_val=y_train[:10000]
partial_y_train=y_train[10000:]


history=model.fit(partial_x_train,
                  partial_y_train,
                  epochs=20,
                  batch_size=512,
                  validation_data=(x_val,y_val))

#绘制训练损失和验证损失
import matplotlib.pyplot as plt
history_dict=history.history
loss_values=history_dict['loss']
val_loss_values=history_dict['val_loss']
epochs=range(1,len(loss_values)+1)
plt.plot(epochs, loss_values, 'ro-', label='Training loss')
plt.plot(epochs, val_loss_values, 'bo-', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()

#绘制训练精度和验证精度
plt.clf()
acc = history_dict['accuracy']
val_acc = history_dict['val_accuracy']
plt.plot(epochs, acc, 'ro-', label='Training accuracy')
plt.plot(epochs, val_acc, 'bo-', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()
