keras_cifar10

import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
import pandas as pd 
import numpy as np
(x_train,y_train),(x_test,y_test1) = cifar10.load_data()
print(x_train.shape,y_train.shape)
print(x_test.shape,y_test1.shape)
import matplotlib.pyplot as plt

y_train = y_train.reshape(y_train.shape[0])
y_test = y_test1.reshape(y_test1.shape[0])
print(x_train.shape,y_train.shape)
print(x_test.shape,y_test.shape)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)

print(x_train.shape,y_train.shape)
print(x_test.shape,y_test.shape)

model = Sequential()

model.add(Conv2D(filters=32,kernel_size=(3,3),
                 input_shape=(32, 32,3), 
                 activation='relu', 
                 padding='same'))
model.add(Dropout(rate=0.25))
model.add(MaxPooling2D(pool_size=(2, 2))) # 16* 16y_test

model.add(Conv2D(filters=64, kernel_size=(3, 3), 
                 activation='relu', padding='same'))
model.add(Dropout(0.25))
model.add(MaxPooling2D(pool_size=(2, 2))) # 8 * 8

model.add(Flatten()) 
model.add(Dropout(rate=0.25))
model.add(Dense(1024, activation='relu')) # FC2 1024
model.add(Dropout(rate=0.25))
model.add(Dense(10, activation='softmax')) # Output 10

model.summary()

opt = keras.optimizers.rmsprop(lr=0.0001,decay=1e-6)

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

datagen = ImageDataGenerator(
    rotation_range = 20,
    zoom_range = 0.15,
    horizontal_flip = True,
)

train_history=model.fit(x=x_train,y=y_train,validation_split=0.3,epochs=100,batch_size=100,verbose=1)

#model.save('cifar10_trained_model.h5')



import matplotlib.pyplot as plt 


#print(x_img_train.shape,x_img_train[0][0][0])
def show_train_history(train_history,train,Validation):
    plt.plot(train_history.history[train])
    plt.plot(train_history.history[Validation])
    plt.title('Train_history')
    plt.ylabel(train)
    plt.xlabel('Epoch')
    plt.legend(['train','validation'],loc = 'upper left')
    plt.show()



scores = model.evaluate(x_test,y_test,verbose=0)
#model.save("cifarCnnModel.h5")
#print("Save mode to disk")
prediction= model.predict_classes(x_test)
print("scores",scores[1])
show_train_history(train_history,'loss','val_loss')
def plot_image(image,labels,prediction,idx,num=10): 
    fig = plt.gcf()
    fig.set_size_inches(12, 14)
    if num>25:
        num=25
    for i in range(0, num):
        ax = plt.subplot(5,5, 1+i)
        ax.imshow(image[idx], cmap='binary')
        title = "label=" +str(labels[idx])
        if len(prediction)>0:
            title+=",perdict="+str(prediction[idx])
        ax.set_title(title,fontsize=10)
        ax.set_xticks([]);ax.set_yticks([])
        idx+=1
    plt.show()
plot_image(x_test,y_test1,prediction,idx=5)
p =confusion_amtrix =pd.crosstab(np.array(y_test1),prediction, rownames=['label'],colnames=['perdict'],margins = True)
print(p)