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X = X/255 #normalize

model = Sequential() #Sequential Model

#Built our Model
# Convolutional2D cause we have Grayscaled Images with 64 neurons
# Size of the window (3x3)

#This is our first Layer
model.add(Conv2D(64, (3,3), input_shape = X.shape[1:])) 
			ZEILE 11 ---->model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

#This is our second Layer
model.add(Conv2D(64, (3,3)) 
			Zeile 16 ----> model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten()) #Flatten to 1D because a Dense Layer wants a 1D Dataset and we have a 2D
model.add(Dense(1))

#Outputlayer
model.add(Activation('sigmoid'))

model.compile(loss="binary_crossentropy",
              optimizer="adam", 
              metrics=['accuracy'])
#Train
model.fit(X,Y, batch_size=32, epochs=3, validation_split=0.1)