from keras.datasets import cifar10
from keras.models import Sequential, save_model
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
import numpy as np
from keras.utils import np_utils
from keras.layers.normalization import BatchNormalization
import loader3
import matplotlib.pyplot as plt
batch_size = 28
num_classes = 2
epochs = 10
train_image = 'D:\\data\\leafs\\train_resize\\'
test_image = 'D:\\data\\leafs\\test_resize\\'
train_label = 'D:\\data\\leafs\\train_label.csv'
test_label = 'D:\\data\\leafs\\test_label.csv'
x_train = loader3.image_load(train_image)
y_train = loader3.label_load(train_label)
x_test = loader3.image_load(test_image)
y_test = loader3.label_load(test_label)
print ( loader3.image_load(train_image).shape )
print ( loader3.image_load(test_image).shape )
print ( loader3.label_load(train_label).shape)
print ( loader3.label_load(test_label).shape )
#(x_train, y_train), (x_test, y_test) = cifar10.load_data()
# One hot Encoding
#y_train = np_utils.to_categorical(y_train)
#y_test = np_utils.to_categorical(y_test)
model = Sequential()
model.add(Conv2D(32, (5, 5), padding='same', input_shape=x_train.shape[1:]))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(32, (5, 5)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
hist = model.fit(x_train, y_train, validation_data=(x_test, y_test), nb_epoch=epochs, batch_size=batch_size, verbose=2)
scores = model.evaluate(x_test, y_test, verbose=0)
print("CNN Error: %.2f%%" % (100-scores[1]*100))
save_model(model, "D:\\data\\leafs\\leaf.h5")
# 학습 정확성 값과 검증 정확성 값을 플롯팅 합니다.
plt.plot(hist.history['accuracy'])
plt.plot(hist.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
# 학습 손실 값과 검증 손실 값을 플롯팅 합니다.
plt.plot(hist.history['loss'])
plt.plot(hist.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()