import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dropout,Dense,Flatten
from keras.layers import Conv2D,MaxPool2D
from keras.optimizers import SGD

x_train=np.random.random([10000,100,100,3])
y_train = keras.utils.to_categorical(np.random.randint(10,size=(10000,1)),num_classes=10)
x_test = np.random.random([200,100,100,3])
y_test = keras.utils.to_categorical(np.random.randint(10,size=(200,1)),num_classes=10)

model = Sequential()
model.add(Conv2D(32,(3,3),activation='relu',input_shape=(100,100,3)))
model.add(Conv2D(32,kernel_size=(3,3),activation='relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Conv2D(64,(3,3),activation='relu'))
model.add(Conv2D(64,(3,3),activation='relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(256,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10,activation='softmax'))

sgd = SGD(lr=0.001,decay=1e-6,momentum=0.7,nesterov=True)
model.compile(loss='categorical_crossentropy',optimizer=sgd)

model.fit(x_train,y_train,batch_size=32,epochs=10)
score = model.evaluate(x_test,y_test,batch_size=32)
