How to implement One-Shot learning tasks in Keras?

In Keras, implementing a One-Shot learning task typically involves using the Siamese neural network architecture. The Siamese neural network is a dual-tower structure where two identical sub-networks share parameters to compare the similarity between two inputs.

Here are the general steps for implementing a One-Shot learning task in Keras:

1. The basic structure of a Siamese neural network is defined as:

from keras.models import Model
from keras.layers import Input, Conv2D, Flatten, Dense

def create_siamese_network(input_shape):
    input_layer = Input(shape=input_shape)
    
    conv1 = Conv2D(32, (3, 3), activation='relu')(input_layer)
    # Add more convolutional layers if needed
    
    flattened = Flatten()(conv1)
    
    dense1 = Dense(128, activation='relu')(flattened)
    
    model = Model(inputs=input_layer, outputs=dense1)
    
    return model

1. Create an instance of a Siamese network and share parameters.

input_shape = (28, 28, 1)
siamese_network = create_siamese_network(input_shape)

input_a = Input(shape=input_shape)
input_b = Input(shape=input_shape)

output_a = siamese_network(input_a)
output_b = siamese_network(input_b)

1. Write a loss function to compute the similarity between two inputs.

from keras import backend as K

def euclidean_distance(vects):
    x, y = vects
    sum_square = K.sum(K.square(x - y), axis=1, keepdims=True)
    return K.sqrt(K.maximum(sum_square, K.epsilon()))

def eucl_dist_output_shape(shapes):
    shape1, shape2 = shapes
    return (shape1[0], 1)

distance = Lambda(euclidean_distance, output_shape=eucl_dist_output_shape)([output_a, output_b])

1. Compile the model and train it.

from keras.models import Model
from keras.layers import Lambda
from keras.optimizers import Adam

siamese_model = Model(inputs=[input_a, input_b], outputs=distance)

siamese_model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])

siamese_model.fit([X_train_pairs[:, 0], X_train_pairs[:, 1]], y_train, batch_size=128, epochs=10)

During the training process, it is necessary to prepare training data that consists of pairs of positive and negative samples. Positive sample pairs represent two samples from the same category, while negative sample pairs represent two samples from different categories. Here, X_train_pairs are the input sample pairs, and y_train is the corresponding label.