FTIR_SiameseNN_metaLearning.py

import numpy as np
from keras.models import Model
from keras.layers import Input, Conv1D, MaxPooling1D, Flatten, Dense
from keras.optimizers import Adam

# define the base Siamese network
def build_siamese_base(input_shape):
    input = Input(shape=input_shape)
    x = Conv1D(filters=32, kernel_size=3, activation='relu')(input)
    x = MaxPooling1D(pool_size=2)(x)
    x = Conv1D(filters=64, kernel_size=3, activation='relu')(x)
    x = MaxPooling1D(pool_size=2)(x)
    x = Flatten()(x)
    x = Dense(128, activation='relu')(x)
    model = Model(inputs=input, outputs=x)
    return model

# define the meta-learning model
def build_meta_model(input_shape, base_model):
    input = Input(shape=input_shape)
    features = base_model(input)
    x = Dense(64, activation='relu')(features)
    output = Dense(1, activation='sigmoid')(x)
    model = Model(inputs=input, outputs=output)
    return model

# create the Siamese base network
input_shape = (100, 1)  # input shape for the network
base_model = build_siamese_base(input_shape)

# create the meta-learning model
meta_input_shape = (100, 2)  # input shape for meta learning
meta_model = build_meta_model(meta_input_shape, base_model)

# compile the meta-learning model
meta_model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.001), metrics=['accuracy'])

# create some sample data for training and testing
x_train = np.random.randn(1000, 100, 2)  # pairs of 100-length 1D signals
y_train = np.random.randint(2, size=1000)  # binary labels (0 or 1)

# train the meta-learning model on the sample data
meta_model.fit(x_train, y_train, epochs=10, batch_size=32, validation_split=0.2)

# generate some test data
x_test = np.random.randn(100, 100, 2)  # pairs of 100-length 1D signals
y_test = np.random.randint(2, size=100)  # binary labels (0 or 1)

# evaluate the meta-learning model on the test data
loss, accuracy = meta_model.evaluate(x_test, y_test, batch_size=32)
print('Test loss:', loss)
print('Test accuracy:', accuracy)