Keras CNN Convolution Neural Network (III.)

To import the desired lib:

Import NumPy as NP from
keras.datasets import mnist to
keras.utils import np_utils from
keras.models Import Sequential
from keras.optimizers import Adam
from keras.layers import dense,activation,convolution2d, Maxpooling2d,flatten,dropout

To set a random seed:

Np.random.seed (1337) #设置随机种子

Load data:

(X_train,y_train), (x_test,y_test) =mnist.load_data () #加载数据

Data preprocessing:

#数据预处理
X_train=x_train.reshape ( -1,28,28,1) #  (n, Monochrome Road, 28,28)
x_test=x_test.reshape ( -1,28,28,1) 
Y_ Train=np_utils.to_categorical (y_train,num_classes=10)
y_test=np_utils.to_categorical (y_test,num_classes=10 )

Standardization:

# convert X_train, x_test data format to float32
X_train = X_train.astype (' float32 ')
x_test = X_test.astype (' float32 ')
# Normalized
x_train/= 255
x_test/= 255

To create a model:

#建模
Model =sequential ()

Build Layer:

#卷积 1
-Layer Model.add (convolution2d (
    filters=32, # 32 filters-) generates 32 depth
    kernel_size=3, # Filter window size (3,3)
    strides= 1,
    padding= ' same ', # Filter Mode
#     activation= ' Relu ',    # Activate function (which can be written here)
    input_shape= (28,28,1),  # input shape is picture shape
)
Model.add (Activation (' Relu ')) #激活函数

#池化 1-Layer
model.add (maxpooling2d (
    pool_size= (2, 2),  # Pool Scan window size
    strides=2,   # Scan window Step 2 for each move 
    padding= ' same ' #默认 is valid
))

#卷积 2
-layer Model.add (convolution2d (
    filters=64, # 64 filters-) generates 64 depth
    kernel_size=3, # Filter window size (3,3)
    padding= ' Same ', # Filter mode
    activation= ' Relu ',    # Activate function (can be written here)
)

#池化 2-layer
Model.add (maxpooling2d (
    pool_size= (2, 2),  # Pool Scan window size
    strides=2,
    padding= ' same ', #默认 is Valid)
)
Model.add (Dropout (0.25)) #防止过拟合

#扁平化
Model.add (Flatten ())

#全连接
Model.add (dense (128))
Model.add (Activation (' Relu '))
Model.add (Dropout (0.5))

Model.add (Dense
) Model.add (Activation (' Softmax '))

#自己定义 optimizer
Adam=adam (lr=1e-4)

# Output model parameter information
model.summary ()

Build the Model:

#搭建模型
model.compile (optimizer= ' Adam ', loss= ' categorical_crossentropy ', metrics=[' accuracy '])

Training:

Model.fit (x_train,y_train,epochs=1,batch_size=32)
#评估
loss,accuracy=model.evaluate (X_test,Y_test)
Print (loss)
print (accuracy)

Results:

10000/10000 [==============================]-eta:0s
0.0454092171811
0.9847