1-d resnet in python

Here's an example implementation of a 1-D ResNet in Python using Keras:


main.py
from keras.models import Model
from keras.layers import Input, Conv1D, BatchNormalization, Activation, Add

def residual_block(x, filters, kernel_size, dilation_rate):
    res = x
    x = Conv1D(filters=filters, kernel_size=kernel_size, padding='same', dilation_rate=dilation_rate)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv1D(filters=filters, kernel_size=kernel_size, padding='same', dilation_rate=dilation_rate)(x)
    x = BatchNormalization()(x)
    x = Add()([x, res])
    x = Activation('relu')(x)
    return x

def build_resnet(input_shape, residual_layers=8, filters=64, kernel_size=3, dilation_rate=1):
    inputs = Input(shape=input_shape)
    x = inputs
    for i in range(residual_layers):
        x = residual_block(x, filters=filters, kernel_size=kernel_size, dilation_rate=dilation_rate)
    outputs = Conv1D(filters=1, kernel_size=1)(x)
    model = Model(inputs, outputs)
    return model
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The residual_block() function defines a single residual block with the specified number of filters, kernel size, and dilation rate. The build_resnet() function creates a ResNet model with the given input shape, number of residual layers, and other hyperparameters. The model uses 1x1 convolution for the final output layer, which maps the feature maps to a 1D output.

You can use this ResNet model to train on your own dataset, for example using the following code:


main.py
from keras.optimizers import Adam
from keras.losses import MeanSquaredError

model = build_resnet(input_shape=(None, 1))
model.compile(optimizer=Adam(lr=0.01), loss=MeanSquaredError())

x_train = ... # shape (num_samples, num_timesteps, 1)
y_train = ... # shape (num_samples, 1)

model.fit(x_train, y_train, batch_size=32, epochs=10)
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Here, x_train is your training data with shape (num_samples, num_timesteps, 1), and y_train is the corresponding target output with shape (num_samples, 1). You can adjust the batch size, number of epochs, learning rate, and other hyperparameters as needed for your specific task.

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