deep learning with keras (二) lenet 对手写数字识别

# import the necessary packages
from keras import backend as K
from keras.models import Sequential
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.core import Activation
from keras.layers.core import Flatten
from keras.layers.core import Dense
from keras.datasets import mnist
from keras.utils import np_utils
from keras.optimizers import SGD, RMSprop, Adam
import numpy as np

import matplotlib.pyplot as plt

np.random.seed(1671)  # for reproducibility

#define the convnet 
class LeNet:
    @staticmethod
    def build(input_shape, classes):
        model = Sequential()
        # CONV => RELU => POOL
        model.add(Conv2D(20, kernel_size=5, padding="same",
            input_shape=input_shape))
        model.add(Activation("relu"))
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
        # CONV => RELU => POOL
        model.add(Conv2D(50, kernel_size=5, padding="same"))
        model.add(Activation("relu"))
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
        # Flatten => RELU layers
        model.add(Flatten())
        model.add(Dense(500))
        model.add(Activation("relu"))

        # a softmax classifier
        model.add(Dense(classes))
        model.add(Activation("softmax"))

        return model

# network and training
NB_EPOCH = 20
BATCH_SIZE = 128
VERBOSE = 1
OPTIMIZER = Adam()
VALIDATION_SPLIT=0.2

IMG_ROWS, IMG_COLS = 28, 28 # input image dimensions
NB_CLASSES = 10  # number of outputs = number of digits
INPUT_SHAPE = (1, IMG_ROWS, IMG_COLS)

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

# consider them as float and normalize
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255 
X_test /= 255  

# we need a 60K x [1 x 28 x 28] shape as input to the CONVNET
X_train = X_train[:, np.newaxis, :, :]
X_test = X_test[:, np.newaxis, :, :]

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)

# initialize the optimizer and model
model = LeNet.build(input_shape=INPUT_SHAPE, classes=NB_CLASSES)
model.compile(loss="categorical_crossentropy", optimizer=OPTIMIZER,
    metrics=["accuracy"])

history = model.fit(X_train, y_train, 
        batch_size=BATCH_SIZE, epochs=NB_EPOCH, 
        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()