from keras.datasets import cifar10
import matplotlib.pyplot as plt
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPool2D, ZeroPadding2D
import numpy as np
import pandas as pd

np.random.seed(10)

(x_img_train, y_label_train), (x_img_test, y_label_test) = cifar10.load_data()

label_dict = {0: "airplane", 1: "automobile", 2: "bird", 3: "cat", 4: "deer", 5: "dog", 6: "frog", 7: "horse",
              8: "ship", 9: "truck"}


def plot_images_labels_prediction(images, 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(images[idx], cmap='binary')
        title = "label=" + str(label_dict[labels[i][0]])
        if len(prediction) > 0:
            title += ",predict=" + str(label_dict[prediction[i]])
        ax.set_title(title, fontsize=10)
        ax.set_xticks([]);
        ax.set_yticks([])
        idx += 1
    plt.show()


print('train_data:images:', x_img_train.shape, 'labels:', y_label_train.shape)
print('test_data:images:', x_img_test.shape, 'labels:', y_label_test.shape)

x_img_train_normalize = x_img_train.astype('float32') / 255.0
x_img_test_normalize = x_img_test.astype('float32') / 255.0

y_label_train_OneHot = np_utils.to_categorical(y_label_train)
y_label_test_OneHot = np_utils.to_categorical(y_label_test)

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(MaxPool2D(pool_size=(2, 2)))

model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(Dropout(rate=0.25))
model.add(MaxPool2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dropout(rate=0.25))

model.add(Dense(1024, activation='relu'))
model.add(Dropout(rate=0.25))

model.add(Dense(10, activation='softmax'))

print(model.summary())

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
train_history = model.fit(x_img_train_normalize, y_label_train_OneHot, validation_split=0.2, epochs=10, batch_size=128,
                          verbose=1)


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()


show_train_history(train_history, 'acc', 'val_acc')
show_train_history(train_history, 'loss', 'val_loss')

scores = model.evaluate(x_img_test_normalize, y_label_test_OneHot, verbose=0)

print(scores[1])

prediction = model.predict_classes(x_img_test_normalize)

print(prediction[:10])

plot_images_labels_prediction(x_img_test, y_label_test, prediction, 0, 10)

Predicted_Probability = model.predict(x_img_test_normalize)
print(Predicted_Probability)

def show_Predicted_Probability(y, prediction, x_img, Predicted_Probability, i):
    print('label:', label_dict[y[i][0]], 'predict', label_dict[prediction[i]])
    plt.figure(figsize=(2, 2))
    plt.imshow(np.reshape(x_img_test[i], (32, 32, 3)))
    plt.show()
    for j in range(10):
        print(label_dict[j] + ' probability:%1.9f' % (Predicted_Probability[i][j]))


show_Predicted_Probability(y_label_test, prediction, x_img_test, Predicted_Probability, 0)

show_Predicted_Probability(y_label_test, prediction, x_img_test, Predicted_Probability, 3)

print(label_dict)
print(pd.crosstab(y_label_test.reshape(-1),prediction,rownames=['label'],colnames=['predict']))