ConvNN

ConvNN

class model:
    def __init__(self,outcomes=10):
        self.model = Sequential()
        self.model.add(Conv2D(28, (3, 3), activation='relu', input_shape=(28, 28, 1)))
        self.model.add(MaxPooling2D((2, 2)))
        self.model.add(Conv2D(64, (3, 3), activation='relu'))
        self.model.add(MaxPooling2D((2, 2)))
        self.model.add(Conv2D(64, (3, 3), activation='relu'))
        self.model.add(Flatten())
        self.model.add(Dense(outcomes, activation='sigmoid')) #sigmoid is good for binary
        self.model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

    def train(self,x,y,epochs=30,batch=32):
        history=self.model.fit(x,y,batch_size=batch,epochs=epochs,validation_split=0.1)
        self.history = history.history #gather training log

    def test(self,X,y):
        assert len(X)==len(y), "Error, the arrays do not match length"
        predictions = self.model.predict(X)
        count=0

        for i in range(len(predictions)):
            pred=np.argmax(predictions[i])
            if y[i]==pred:
                count+=1
        return count/len(predictions)
    def save(self,name):
        self.model.save(''+name)