tensorflow python3 minist keras简单卷积网络实现

https://github.com/sofiathefirst/AIcode/tree/master/03minstDemo

 

 

from __future__ import print_function
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation,Flatten
from keras.optimizers import RMSprop
from keras.utils import np_utils
from keras.layers.convolutional import Conv2D, MaxPooling2D
import matplotlib.pyplot as plt

np.random.seed(1671)  # for reproducibility

# network and training
NB_EPOCH = 20
BATCH_SIZE = 128
VERBOSE = 1
NB_CLASSES = 10   # number of outputs = number of digits
OPTIMIZER = RMSprop() # optimizer, explainedin this chapter
N_HIDDEN = 128
VALIDATION_SPLIT=0.2 # how much TRAIN is reserved for VALIDATION
DROPOUT = 0.3

# data: shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()

#X_train is 60000 rows of 28x28 values --> reshaped in 60000 x 784
RESHAPED = 784
#
X_train = X_train.reshape(60000, 28,28,1)
X_test = X_test.reshape(10000, 28,28,1)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')

# normalize 
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
Y_train = np_utils.to_categorical(y_train, NB_CLASSES)
Y_test = np_utils.to_categorical(y_test, NB_CLASSES)

# M_HIDDEN hidden layers
# 10 outputs
# final stage is softmax

model = Sequential()
model.add(Conv2D(32, kernel_size=5, padding='same',
                        input_shape=(28, 28, 1)))#32个卷积核 ， 
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))#池化层
model.add(Dropout(0.25))#随机丢弃层


model.add(Conv2D(64, kernel_size=5, padding='same'))#64个卷积核
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))#池化层
model.add(Dropout(0.25))#随机丢弃层


model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(NB_CLASSES))
model.add(Activation('softmax'))



model.summary()

model.compile(loss='categorical_crossentropy',
              optimizer=OPTIMIZER,
              metrics=['accuracy'])

history = model.fit(X_train, Y_train,
                    batch_size=BATCH_SIZE, epochs=6,
                    verbose=VERBOSE, validation_split=VALIDATION_SPLIT)

score = model.evaluate(X_test, Y_test, verbose=VERBOSE)
print("\nTest score:", score[0])
print('Test accuracy:', score[1])

# list all data in history
print(history.history.keys())
# summarize history for accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()

model summary

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 28, 28, 32)        832       
_________________________________________________________________
activation_1 (Activation)    (None, 28, 28, 32)        0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 14, 14, 32)        0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 14, 14, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 14, 14, 64)        51264     
_________________________________________________________________
activation_2 (Activation)    (None, 14, 14, 64)        0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 7, 7, 64)          0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 7, 7, 64)          0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 3136)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 1024)              3212288   
_________________________________________________________________
activation_3 (Activation)    (None, 1024)              0         
_________________________________________________________________
dropout_3 (Dropout)          (None, 1024)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 10)                10250     
_________________________________________________________________
activation_4 (Activation)    (None, 10)                0         
=================================================================
Total params: 3,274,634