From 33940965f7812decca45b5e23ebafcaaff243d10 Mon Sep 17 00:00:00 2001
From: leufen1 <l.leufen@fz-juelich.de>
Date: Fri, 12 Mar 2021 12:02:20 +0100
Subject: [PATCH] first CNN class try
---
mlair/model_modules/convolutional_networks.py | 113 ++++++++++++++++++
1 file changed, 113 insertions(+)
create mode 100644 mlair/model_modules/convolutional_networks.py
diff --git a/mlair/model_modules/convolutional_networks.py b/mlair/model_modules/convolutional_networks.py
new file mode 100644
index 00000000..f9acdb72
--- /dev/null
+++ b/mlair/model_modules/convolutional_networks.py
@@ -0,0 +1,113 @@
+__author__ = "Lukas Leufen"
+__date__ = '2021-02-'
+
+from functools import reduce, partial
+
+from mlair.model_modules import AbstractModelClass
+from mlair.helpers import select_from_dict
+from mlair.model_modules.loss import var_loss, custom_loss
+from mlair.model_modules.advanced_paddings import PadUtils, Padding2D, SymmetricPadding2D
+
+import keras
+
+
+class CNN(AbstractModelClass):
+ _activation = {"relu": keras.layers.ReLU, "tanh": partial(keras.layers.Activation, "tanh"),
+ "sigmoid": partial(keras.layers.Activation, "sigmoid"),
+ "linear": partial(keras.layers.Activation, "linear"),
+ "selu": partial(keras.layers.Activation, "selu")}
+ _initializer = {"selu": keras.initializers.lecun_normal()}
+ _optimizer = {"adam": keras.optimizers.adam}
+ _regularizer = {"l1": keras.regularizers.l1, "l2": keras.regularizers.l2, "l1_l2": keras.regularizers.l1_l2}
+ _requirements = ["lr", "beta_1", "beta_2", "epsilon", "decay", "amsgrad"]
+
+ def __init__(self, input_shape: list, output_shape: list, activation="relu", activation_output="linear",
+ optimizer="adam", regularizer=None, **kwargs):
+
+ assert len(input_shape) == 1
+ assert len(output_shape) == 1
+ super().__init__(input_shape[0], output_shape[0])
+
+ # settings
+ self.activation = self._set_activation(activation)
+ self.activation_name = activation
+ self.activation_output = self._set_activation(activation_output)
+ self.activation_output_name = activation_output
+ self.kernel_initializer = self._initializer.get(activation, "glorot_uniform")
+ self.kernel_regularizer = self._set_regularizer(regularizer, **kwargs)
+ self.optimizer = self._set_optimizer(optimizer, **kwargs)
+
+ # apply to model
+ self.set_model()
+ self.set_compile_options()
+ self.set_custom_objects(loss=custom_loss([keras.losses.mean_squared_error, var_loss]), var_loss=var_loss)
+
+ def _set_activation(self, activation):
+ try:
+ return self._activation.get(activation.lower())
+ except KeyError:
+ raise AttributeError(f"Given activation {activation} is not supported in this model class.")
+
+ def _set_optimizer(self, optimizer, **kwargs):
+ try:
+ opt_name = optimizer.lower()
+ opt = self._optimizer.get(opt_name)
+ opt_kwargs = {}
+ if opt_name == "adam":
+ opt_kwargs = select_from_dict(kwargs, ["lr", "beta_1", "beta_2", "epsilon", "decay", "amsgrad"])
+ return opt(**opt_kwargs)
+ except KeyError:
+ raise AttributeError(f"Given optimizer {optimizer} is not supported in this model class.")
+
+ def _set_regularizer(self, regularizer, **kwargs):
+ if regularizer is None or (isinstance(regularizer, str) and regularizer.lower() == "none"):
+ return None
+ try:
+ reg_name = regularizer.lower()
+ reg = self._regularizer.get(reg_name)
+ reg_kwargs = {}
+ if reg_name in ["l1", "l2"]:
+ reg_kwargs = select_from_dict(kwargs, reg_name, remove_none=True)
+ if reg_name in reg_kwargs:
+ reg_kwargs["l"] = reg_kwargs.pop(reg_name)
+ elif reg_name == "l1_l2":
+ reg_kwargs = select_from_dict(kwargs, ["l1", "l2"], remove_none=True)
+ return reg(**reg_kwargs)
+ except KeyError:
+ raise AttributeError(f"Given regularizer {regularizer} is not supported in this model class.")
+
+ def set_model(self):
+ """
+ Build the model.
+ """
+ x_input = keras.layers.Input(shape=self._input_shape)
+ kernel = (1, 1)
+ pad_size = PadUtils.get_padding_for_same(kernel)
+ x_in = Padding2D("SymPad2D")(padding=pad_size, name="SymPad")(x_input)
+ x_in = keras.layers.Conv2D(filters=16, kernel_size=kernel,
+ kernel_initializer=self.kernel_initializer,
+ kernel_regularizer=self.kernel_regularizer)(x_in)
+ x_in = self.activation()(x_in)
+ x_in = keras.layers.Conv2D(filters=32, kernel_size=kernel,
+ kernel_initializer=self.kernel_initializer,
+ kernel_regularizer=self.kernel_regularizer)(x_in)
+ x_in = self.activation()(x_in)
+ x_in = Padding2D("SymPad2D")(padding=pad_size, name="SymPad")(x_in)
+ x_in = keras.layers.Conv2D(filters=64, kernel_size=kernel,
+ kernel_initializer=self.kernel_initializer,
+ kernel_regularizer=self.kernel_regularizer)(x_in)
+ x_in = self.activation()(x_in)
+ x_in = keras.layers.Flatten()(x_in)
+ x_in = keras.layers.Dense(64, kernel_initializer=self.kernel_initializer,
+ kernel_regularizer=self.kernel_regularizer)(x_in)
+ x_in = self.activation()(x_in)
+ x_in = keras.layers.Dense(16, kernel_initializer=self.kernel_initializer,
+ kernel_regularizer=self.kernel_regularizer)(x_in)
+ x_in = self.activation()(x_in)
+ x_in = keras.layers.Dense(self._output_shape)(x_in)
+ out = self.activation_output(name=f"{self.activation_output_name}_output")(x_in)
+ self.model = keras.Model(inputs=x_input, outputs=[out])
+
+ def set_compile_options(self):
+ self.compile_options = {"loss": [custom_loss([keras.losses.mean_squared_error, var_loss])],
+ "metrics": ["mse", "mae", var_loss]}
--
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