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Create a sequential model\n    model = Sequential([\n        # Input layer specifies the shape of the input data\n        Input(shape=input_shape),\n        # First convolution layer with 128 filters\n        Conv2D(128, (3, 3), padding='same'),\n        BatchNormalization(),\n        Activation(activation),\n        MaxPooling2D((2, 2)),\n        # Second convolution layer with 256 filters\n        Conv2D(256, (3, 3), padding='same'),\n        BatchNormalization(),\n        Activation(activation),\n        MaxPooling2D((2, 2)),\n        # Flatten the output from 2D to 1D before passing to the dense layer\n        Flatten(),\n        Dense(2048, activation='relu', kernel_regularizer=l2(0.01)),\n        Dropout(0.5),\n        # Output layer with linear activation to predict a continuous value\n        Dense(1, activation='linear')\n    ])\n    # Compile the model with Adam optimizer and mean squared error loss\n    model.compile(optimizer=Adam(learning_rate=0.0001), loss='mean_squared_error', metrics=['mae'])\n    # Return the model\n    return model\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftynab%2Fanimal-chess","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftynab%2Fanimal-chess","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftynab%2Fanimal-chess/lists"}