私の最初のDL

以下のコードは『Python深さ学習』から

#Author:KXG
#   imdb   
from keras.datasets import imdb
#         10000        ，        
(train_data,train_lables),(test_data,test_lables)=imdb.load_data(path='./imdb.npz',num_words=10000)#
# print(train_data[0])
#             
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)
y_train=vectorize_sequences(test_data)
#     
y_train=np.asarray(train_lables).astype('float32')
y_test=np.asarray(test_lables).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'))

#      
x_val=x_train[:10000]
partial_x_train=x_train[10000:]
y_val=y_train[:10000]
partial_y_train=y_train[10000:]
#     
#     
model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['accuracy'])
history=model.fit(partial_x_train,
                  partial_y_train,
                  epochs=20,
                  batch_size=512,
                  validation_data=(x_val,y_val))
#            
import matplotlib.pyplot as plt
history_dict=history.history
# print(history_dict.keys())
loss_values=history_dict['loss']
val_loss_values=history_dict['val_loss']

epochs=range(1,len(loss_values)+1)
plt.plot(epochs,loss_values,'bo',label='Training loss')
plt.plot(epochs,val_loss_values,'b',label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()
#            
plt.clf()#    
acc=history_dict['accuracy']
val_acc=history_dict['val_accuracy']
plt.plot(epochs,acc,'bo',label='Training acc')
plt.plot(epochs,val_acc,'b',label='Validation acc')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()#    
plt.show()