Siamese model accuracy stuck at 0.5

Question

Siamese model accuracy stuck at 0.5

Alex Aramyan

2022年4月2日 10:15

I'm trying to train a Siamese Network model for a signatures dataset using Keras API and considering the loss only seems not bad. But ironically enough the model accuracy stuck at 0.5.

Model Loss:

Model Accuracy:

My model is kind of a deep model, here's its architecture:

input = Input((128, 128, 1))

x = BatchNormalization()(input)
x = Conv2D(16, (2, 2), activation=tanh)(x)
x = AveragePooling2D(pool_size=(2, 2))(x)
x = Conv2D(32, (2, 2), activation=tanh)(x)
x = AveragePooling2D(pool_size=(2, 2))(x)
x = Conv2D(64, (2, 2), activation=tanh)(x)
x = AveragePooling2D(pool_size=(2, 2))(x)
x = Conv2D(128, (2, 2), activation=tanh)(x)
x = AveragePooling2D(pool_size=(2, 2))(x)
x = Conv2D(256, (2, 2), activation=tanh)(x)
x = AveragePooling2D(pool_size=(2, 2))(x)
x = Flatten()(x)

x = BatchNormalization()(x)
x = Dense(10, activation=tanh, kernel_regularizer=l2)(x)

embedding_network = Model(input, x)

input_1 = Input((128, 128, 1))
input_2 = Input((128, 128, 1))

tower_1 = embedding_network(input_1)
tower_2 = embedding_network(input_2)

merge_layer = Lambda(euclidean_distance)([tower_1, tower_2])
normal_layer = BatchNormalization()(merge_layer)
output_layer = Dense(1, activation=sigmoid)(normal_layer)

siamese = Model(inputs=[input_1, input_2], outputs=output_layer)

The model takes grayscale 128x128 images of signatures in batches of 16 images using the ImageDataGenerator class having 800 training samples and 200 validation samples and trains in 15 epochs. The optimizer function is RMSprop at learning rate of 0.001 and the loss function is Contrasive Loss with margin of 0.5.

What did I miss and why doesn't my model learning?

Topic siamese-networks keras tensorflow deep-learning machine-learning

Category Data Science

Alex Aramyan · Accepted Answer · 2022年4月2日 10:15

Turns out the problem was hidden in the loss function. I decided to change it from Contrasive Loss to Categorical Crossentropy just for fun, and with some kind of "magic" it worked.

from:

def loss(margin=1):
  def contrastive_loss(y_true, y_pred):
    square_pred = tf.math.square(y_pred)
    margin_square = tf.math.square(tf.math.maximum(margin - (y_pred), 0))
    return tf.math.reduce_mean(
        (1 - y_true) * square_pred + (y_true) * margin_square
    )

  return contrastive_loss

siamese.compile(loss=loss(margin=MARGIN), optimizer=RMSprop(learning_rate=0.001), metrics=["accuracy"])

to:

siamese.compile(loss="categorical_crossentropy", optimizer=RMSprop(learning_rate=0.001), metrics=["accuracy"])

Siamese model accuracy stuck at 0.5

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