神经网络——项目二CNN手写数字识别

 2 import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.layers import Convolution2D, MaxPooling2D, Flatten
from keras.optimizers import SGD, Adam
from keras.utils import np_utils
from keras.datasets import mnist
#categrorical_crossentropy

def load_data():
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    
    #x_train = x_train[0:number]
    #y_train = y_train[0:number]
    x_train = x_train.reshape(x_train.shape[0], 1, 28, 28)
    x_test = x_test.reshape(x_test.shape[0], 1, 28, 28)
    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    # convert class vector to binary class matrices
    y_train = np_utils.to_categorical(y_train, 10)
    y_test = np_utils.to_categorical(y_test, 10)
    x_train = x_train
    x_test = x_test
    #x_test = np.random.normal(x_test)
    x_train = x_train / 255
    x_test = x_test / 255
    return (x_train, y_train), (x_test,y_test)

(x_train, y_train), (x_test,y_test) = load_data()


model2 = Sequential()
model2.add(Convolution2D(25, 3, strides=3, input_shape=(1, 28, 28), data_format='channels_first', padding='same'))  
model2.add(MaxPooling2D((2, 2))) 





model2.add(Convolution2D(50, 3, strides=3, padding='same')) 
model2.add(MaxPooling2D(2, 2))   


model2.add(Flatten())

model2.add(Dense(128, activation='relu'))
model2.add(Dense(10, activation='softmax'))

model2.compile(loss='mse', optimizer=SGD(lr=0.1), metrics=['accuracy'])
model2.fit(x_train, y_train, batch_size=200, epochs=10, verbose=1)

result = model2.evaluate(x_test, y_test, verbose=0)

print('
Test Acc:', result)

以上demo是可以运行，但需修改parameters 

过程所遇到的Error总结：

1.Negative dimension size caused by subtracting 3 from 1 for 'conv2d_1/convolution' (op: 'Conv2D') .....

2.ValueError: Negative dimension size caused by subtracting 2 from 1 for 'max_pooling2d_2/MaxPool' (op: 'MaxPool') with input shapes: [?,4,1,50].

解决方案如下：