keras绘制损失函数曲线
# -*- coding: utf-8 -*-
'''Trains a simple deep NN on the MNIST dataset.
Gets to 98.40% test accuracy after 20 epochs
(there is *a lot* of margin for parameter tuning).
2 seconds per epoch on a K520 GPU.
'''
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop
import matplotlib.pyplot as plt
batch_size = 128
num_classes = 10
epochs = 20
# the data, shuffled and split between train and test sets
# (x_train, y_train), (x_test, y_test) = mnist.load_data()
(x_train, y_train), (x_test, y_test) = mnist.load_data(path='/home/duchao/下载/mnist.npz')
# import numpy as np
#
# path = '/home/duchao/下载/mnist.npz'
# f = np.load(path)
# x_train, y_train = f['x_train'], f['y_train']
# x_test, y_test = f['x_test'], f['y_test']
# f.close()
x_train = x_train.reshape(60000, 784).astype('float32')
x_test = x_test.reshape(10000, 784).astype('float32')
x_train /= 255
x_test /= 255
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
# label为0~9共10个类别，keras要求格式为binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
# add by hcq-20171106
# Dense of keras is full-connection.
model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer=RMSprop(),
metrics=['accuracy'])
history = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
history_dict=history.history
loss_value=history_dict["loss"]
val_loss_value=history_dict["val_loss"]
epochs=range(1,len(loss_value)+1)
plt.plot(epochs,loss_value,"bo",label="Training loss")
plt.plot(epochs,val_loss_value,"b",label="Validation loss")
plt.xlabel("epochs")
plt.ylabel("loss")
plt.legend()
plt.show()
# -*- coding: utf-8 -*-
'''Trains a simple deep NN on the MNIST dataset.
Gets to 98.40% test accuracy after 20 epochs
(there is *a lot* of margin for parameter tuning).
2 seconds per epoch on a K520 GPU.
'''
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop
import matplotlib.pyplot as plt
batch_size = 128
num_classes = 10
epochs = 40
# the data, shuffled and split between train and test sets
# (x_train, y_train), (x_test, y_test) = mnist.load_data()
(x_train, y_train), (x_test, y_test) = mnist.load_data(path='/home/duchao/下载/mnist.npz')
# import numpy as np
#
# path = '/home/duchao/下载/mnist.npz'
# f = np.load(path)
# x_train, y_train = f['x_train'], f['y_train']
# x_test, y_test = f['x_test'], f['y_test']
# f.close()
x_train = x_train.reshape(60000, 784).astype('float32')
x_test = x_test.reshape(10000, 784).astype('float32')
x_train /= 255
x_test /= 255
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
# label为0~9共10个类别，keras要求格式为binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
# add by hcq-20171106
# Dense of keras is full-connection.
model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer=RMSprop(),
metrics=['accuracy'])
history = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
# ##绘制训练损失和验证损失
# history_dict=history.history
# loss_value=history_dict["loss"]
# val_loss_value=history_dict["val_loss"]
#
# epochs=range(1,len(loss_value)+1)
# plt.plot(epochs,loss_value,"bo",label="Training loss")
# plt.plot(epochs,val_loss_value,"b",label="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()
history_dict=history.history
acc=history_dict["acc"]
val_acc=history_dict["val_acc"]
loss_value=history_dict["loss"]
val_loss_value=history_dict["val_loss"]
epochs=range(1,len(val_acc)+1)
plt.plot(epochs,acc,"bo",label="Training acc")
plt.plot(epochs,val_acc,"b",label="Validation acc")
plt.plot(epochs,loss_value,"bo",label="Training loss")
plt.plot(epochs,val_loss_value,"b",label="Validation loss")
plt.xlabel("epochs")
plt.ylabel("Accuracy")
plt.legend()
plt.show()
# -*- coding: utf-8 -*-
'''Trains a simple deep NN on the MNIST dataset.
Gets to 98.40% test accuracy after 20 epochs
(there is *a lot* of margin for parameter tuning).
2 seconds per epoch on a K520 GPU.
'''
#
# from keras import layers
# from keras.datasets import boston_housing
#
#
# (train_data, train_labels), (test_data, test_labels) = boston_housing.load_data()
from keras.datasets import boston_housing
from keras import models
from keras import layers
import numpy as np
import matplotlib.pyplot as plt
# train_data.shape:(404, 13),test_data.shape:(102, 13),
# train_targets.shape:(404,),test_targets.shape:(102,)
# the data compromises 13 features
# the targets are the median values of owner-occupied homes,in thousands of dollars
(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()
# feature-wise normalization
mean = train_data.mean(axis=0)
train_data -= mean
std = train_data.std(axis=0)
train_data /= std
# never use any quantity computed on the test data
test_data -= mean
test_data /= std
# build the model
# because we need to build a model several times,we use function to cons
def build_model():
model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape=(train_data.shape[1],)))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(1))
model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
return model
#
# # K-fold validation
# k = 4
# num_val_samples = len(train_data) // k
# num_epochs = 500
# all_scores = []
# all_mae_histories = []
# K-fold validation and logs
k = 4
num_val_samples = len(train_data) // k
num_epochs = 50
all_scores = []
all_mae_histories = []
for i in range(k):
print('正在处理fold #', i)
# preparing the validation data:data from partition #k
val_data = train_data[i * num_val_samples:(i + 1) * num_val_samples]
val_targets = train_targets[i * num_val_samples:(i + 1) * num_val_samples]
# preparing the training data:data from all other partitions
partial_train_data = np.concatenate(
[train_data[:i * num_val_samples],
train_data[((i + 1) * num_val_samples):]],
axis=0
)
partial_train_targets = np.concatenate(
[train_targets[:i * num_val_samples],
train_targets[((i + 1) * num_val_samples):]],
axis=0
)
# build the model
model = build_model()
# train the model,silent mode
history = model.fit(partial_train_data, partial_train_targets, validation_data=(val_data, val_targets),
epochs=num_epochs, batch_size=1, verbose=0)
# evaluate the model in the validation data
mae_history = history.history['val_mean_absolute_error']
val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)
all_scores.append(val_mae)
all_mae_histories.append(mae_history)
print("Complete!")
average_mae_history = [
np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]
mean_score = np.mean(all_scores)
print("mean_score:", mean_score)
#plotting validation scores
plt.plot(range(1,len(average_mae_history)+1),average_mae_history)
plt.xlabel('Epochs')
plt.ylabel('Validation MAE')
plt.show()