import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dense,Dropout,Flatten
from keras.layers import Conv2D,MaxPooling2D
from keras.optimizers import SGD

#Generate dummy data
x_train=np.random.random((100,100,100,3))
y_train=keras.utils.to_categorical(np.random.randint(10,size=(100,1)),num_classes=10)
x_test=np.random.random((20,100,100,3))
y_test=keras.utils.to_categorical(np.random.randint(10,size=(20,1)),num_classes=10)

model=Sequential()
#input :100*100 images with 3 channels -> (100,100,2) tensors
#this applies 32 convolution filters with size 3*3 each
model.add(Conv2D(32,(3,3),activation='relu',input_shape=(100,100,3)))
model.add(Conv2D(32,(3,3),activation='relu'))
model.add(MaxPooling2D(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=0.01,decay=1e-6,momentum=0.9,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)
print(score)