Keras实战（一）——mnist分类问题

1.mnist数据集下载不下来的解决办法：【Windows系统】
下载链接：https://pan.baidu.com/s/1U7KhKOs6f-UvdxRDERGRhA 密码：u6oi
下载下来之后，我又在site-packages底下的keras文件夹新建了一个examples文件夹，将这个下载下来的npz文件放在里面了。
然后在用到的时候指定路径，在load就可以啦：

import numpy as np
import matplotlib.pyplot as plt

path = r"E:\anaconda3\Lib\site-packages\keras\examples\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()
print(x_train.shape)
print(x_test.shape)
for i in range(9):
    plt.subplot(3,3,i+1)
    plt.imshow(x_train[i], cmap='gray', interpolation='none')
    plt.title("Class {}".format(y_train[i]))
plt.show()

结果图：

2.解决完了就可以进行下一步了=。=

from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout
from keras.utils import np_utils
import numpy as np
import matplotlib.pyplot as plt
from keras.optimizers import RMSprop

path = r"E:\anaconda3\Lib\site-packages\keras\examples\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()
# print(x_train.shape)
# print(x_test.shape)
# for i in range(9):
#     plt.subplot(3,3,i+1)
#     plt.imshow(x_train[i], cmap='gray', interpolation='none')
#     plt.title("Class {}".format(y_train[i]))
# plt.show()

#将二维数据变为一维
X_train = x_train.reshape(len(x_train), -1)
X_test = x_test.reshape(len(x_test), -1)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
# Normalization.scaling it so that all values are in the [0, 1] interval. 
X_train = (X_train - 127) / 127
X_test = (X_test - 127) / 127

#one hot encoding
y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)
model = Sequential([
    Dense(512, input_dim=784),
    Activation('relu'),
    Dropout(0.2),
    # Dense(512),
    # Activation('relu'),
    # Dropout(0.2),
    Dense(10),
    Activation('softmax'),

])
rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)

# We add metrics to get more results you want to see
model.compile(optimizer=rmsprop,
              loss='categorical_crossentropy',
              metrics=['accuracy'])

print('Training ------------')
# Another way to train the model
model.fit(X_train, y_train, epochs=20, batch_size=100)

print('\nTesting ------------')
# Evaluate the model with the metrics we defined earlier
loss, accuracy = model.evaluate(X_test, y_test)

print('test loss: ', loss)
print('test accuracy: ', accuracy)

但是不明白每一层设置神经元的数量，以及batch_size和epoch是怎么定的。。
结果：