Keras Model for Siamese Network not Learning and always predicting the same ouput

问题

I am trying to train a Siamese neural network using Keras, with the goal of identifying if 2 images belong to same class or not. My data is shuffled and has equal number of positive examples and negative examples. My model is not learning anything and it is predicting the same output always. I am getting the same loss, validation accuracy, and validation loss every time.

Training Output

def convert(row):
    return imread(row)

def contrastive_loss(y_true, y_pred):
    margin = 1
    square_pred = K.square(y_pred)
    margin_square = K.square(K.maximum(margin - y_pred, 0))
    return K.mean(y_true * square_pred + (1 - y_true) * margin_square)

def SiameseNetwork(input_shape):
    top_input = Input(input_shape)

    bottom_input = Input(input_shape)

    # Network
    model = Sequential()
    model.add(Conv2D(96,(7,7),activation='relu',input_shape=input_shape))
    model.add(MaxPooling2D())
    model.add(Conv2D(256,(5,5),activation='relu',input_shape=input_shape))
    model.add(MaxPooling2D())
    model.add(Conv2D(256,(5,5),activation='relu',input_shape=input_shape))
    model.add(MaxPooling2D())
    model.add(Flatten())
    model.add(Dense(4096,activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1024,activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(512,activation='relu'))
    model.add(Dropout(0.5))

    encoded_top = model(top_input)
    encoded_bottom = model(bottom_input)

    L1_layer = Lambda(lambda tensors:K.abs(tensors[0] - tensors[1]))    
    L1_distance = L1_layer([encoded_top, encoded_bottom])

    prediction = Dense(1,activation='sigmoid')(L1_distance)
    siamesenet = Model(inputs=[top_input,bottom_input],outputs=prediction)
    return siamesenet

data = pd.read_csv('shuffleddata.csv')
print('Converting X1....')

X1 = [convert(x) for x in data['X1']]

print('Converting X2....')

X2 = [convert(x) for x in data['X2']]

print('Converting Y.....')
Y = [0 if data['Y'][i] == 'Negative' else 1 for i in range(len(data['Y']))]

input_shape = (53,121,3,)
model = SiameseNetwork(input_shape)
model.compile(loss=contrastive_loss,optimizer='sgd',metrics=['accuracy'])
print(model.summary())
model.fit(X1,Y,batch_size=32,epochs=20,shuffle=True,validation_split = 0.2)
model.save('Siamese.h5')

回答1:

Mentioning the resolution to this issue in this section (even though it is present in Comments Section), for the benefit of the community.

Since the Model is working fine with other Standard Datasets, the solution is to use more Data. Model is not learning because it has less data for Training.

回答2:

The Model is working fine with more data as mentioned in comments and in the answer by Tensorflow Support. Tweaking the model a little is also working. Changing the number of filters in 2nd and 3rd convolutional layers from 256 to 64 is decreasing the number of trainable parameters by a large number and therefore model started learning.

来源：https://stackoverflow.com/questions/59442922/keras-model-for-siamese-network-not-learning-and-always-predicting-the-same-oupu

标签

python

tensorflow

keras

neural-network