from keras.models import Sequential
from keras.layers import Dense, Flatten, Dropout
from keras.layers.convolutional import Conv2D, MaxPooling2D
import numpy as np
from keras.utils import to_categorical
from keras.optimizers import Adam
seed = 7
np.random.seed(seed)
from keras.datasets import cifar10
cifar10 = cifar10.load_data()
(x_train, y_train), (x_test, y_test) = cifar10
y_train = y_train.reshape(y_train.shape[0])
y_test = y_test.reshape(y_test.shape[0])

x_train = x_train.astype("float32")
x_test = x_test.astype("float32")
x_train /= 255
x_test /=255
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)
print(x_train.shape)
model = Sequential()
model.add(Conv2D(64, (3, 3), strides=(1, 1), input_shape=(32, 32, 3), padding='same', activation='relu',
                 kernel_initializer='uniform'))
model.add(Conv2D(64, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, (3, 2), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
model.add(Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
adam = Adam(lr=1e-5)
model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])
model.fit(x_train, y_train, epochs=1, batch_size=32)