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

Issue

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')

Solution

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.

Answered By - Tensorflow Support