Create README.md
Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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tensorflow/intro/README.md
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tensorflow/intro/README.md
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# Tensorflow / Keras -- A fast non-introduction
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{:.no_toc}
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<nav markdown="1" class="toc-class">
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* TOC
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{:toc}
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</nav>
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## Top
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This is a fast overview how to get an MNIST example running under TF Keras
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If you just want to start with Tensorflow / Keras (especially if it is a scientific project), then you want to reconsider using Keras. In this case please check (& use) PyTorch.
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Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
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## Data loader / Data generator
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|---|---|
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| [keras.utils.Sequence](https://www.tensorflow.org/api_docs/python/tf/keras/utils/Sequence) | "Base object for fitting to a sequence of data, such as a dataset."|
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| [tf.keras.utils.to_categorical](https://www.tensorflow.org/api_docs/python/tf/keras/utils/to_categorical) | "Converts a class vector (integers) to binary class matrix."|
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## Basic
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```python
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from tensorflow import keras
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import numpy as np
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class DataGenerator(keras.utils.Sequence):
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def __init__(
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self,
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train: bool = True,
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size_of_batch: int = 32,
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number_of_classes: int = 10,
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do_shuffle: bool = True,
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) -> None:
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super(DataGenerator, self).__init__()
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if train is True:
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self.pattern_storage: np.ndarray = np.load("./train_pattern_storage.npy")
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self.label_storage: np.ndarray = np.load("./train_label_storage.npy")
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else:
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self.pattern_storage = np.load("./test_pattern_storage.npy")
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self.label_storage = np.load("./test_label_storage.npy")
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self.pattern_storage = self.pattern_storage.astype(np.float32)
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self.pattern_storage /= np.max(self.pattern_storage)
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self.dimensions: tuple[int, int] = (
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self.pattern_storage.shape[1],
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self.pattern_storage.shape[2],
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)
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# How many pattern are there?
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self.number_of_pattern: int = self.label_storage.shape[0]
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self.size_of_batch: int = size_of_batch
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self.number_of_classes: int = number_of_classes
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self.do_shuffle: bool = do_shuffle
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if self.pattern_storage.ndim == 3:
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self.number_of_channel: int = 1
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else:
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self.number_of_channel = self.pattern_storage.shape[3]
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self.available_indices: np.ndarray = np.arange(self.number_of_pattern)
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self.on_epoch_end()
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def on_epoch_end(self) -> None:
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self.available_indices = np.arange(self.number_of_pattern)
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if self.do_shuffle is True:
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np.random.shuffle(self.available_indices)
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def __getitem__(self, index: int) -> tuple[np.ndarray, np.ndarray]:
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selected_indices: np.ndarray = self.available_indices[
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index * self.size_of_batch : (index + 1) * self.size_of_batch
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]
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image, target = self.__data_generation(selected_indices)
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return image, target
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def __data_generation(
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self, list_of_indice: np.ndarray
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) -> tuple[np.ndarray, np.ndarray]:
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image = np.empty(
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(self.size_of_batch, *self.dimensions, self.number_of_channel),
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dtype=np.float32,
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)
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target = np.empty((self.size_of_batch), dtype=int)
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for i in range(0, len(list_of_indice)):
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if self.pattern_storage.ndim == 3:
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image[i, :, :, 0] = self.pattern_storage[
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self.available_indices[list_of_indice[i]], :, :
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]
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else:
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image[i, :, :, :] = self.pattern_storage[
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self.available_indices[list_of_indice[i]], :, :, :
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]
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target[i] = self.label_storage[self.available_indices[list_of_indice[i]]]
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return image, keras.utils.to_categorical(
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target, num_classes=self.number_of_classes
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)
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def __len__(self):
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return int(np.floor(self.number_of_pattern / self.size_of_batch))
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if __name__ == "__main__":
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pass
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```
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## With data augmentation
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To the pre-processing chain **self.data_augmentation** you can add other preprocessing layers which are then applied to the input before given to the network.
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```python
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from tensorflow import keras
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import numpy as np
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class DataGenerator(keras.utils.Sequence):
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def __init__(
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self,
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train: bool = True,
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size_of_batch: int = 32,
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number_of_classes: int = 10,
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do_shuffle: bool = True,
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) -> None:
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super(DataGenerator, self).__init__()
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if train is True:
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self.pattern_storage: np.ndarray = np.load("./train_pattern_storage.npy")
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self.label_storage: np.ndarray = np.load("./train_label_storage.npy")
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else:
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self.pattern_storage = np.load("./test_pattern_storage.npy")
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self.label_storage = np.load("./test_label_storage.npy")
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self.pattern_storage = self.pattern_storage.astype(np.float32)
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self.pattern_storage /= np.max(self.pattern_storage)
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self.dimensions: tuple[int, int] = (
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self.pattern_storage.shape[1],
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self.pattern_storage.shape[2],
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)
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reduction: tuple[int, int] = (4, 4)
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if train is True:
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self.data_augmentation = keras.Sequential(
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[
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keras.layers.RandomCrop(
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height=self.dimensions[0] - reduction[0],
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width=self.dimensions[1] - reduction[1],
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),
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]
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)
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else:
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self.data_augmentation = keras.Sequential(
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[
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keras.layers.CenterCrop(
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height=self.dimensions[0] - reduction[0],
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width=self.dimensions[1] - reduction[1],
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),
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]
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)
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# How many pattern are there?
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self.number_of_pattern: int = self.label_storage.shape[0]
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self.size_of_batch: int = size_of_batch
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self.number_of_classes: int = number_of_classes
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self.do_shuffle: bool = do_shuffle
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if self.pattern_storage.ndim == 3:
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self.number_of_channel: int = 1
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else:
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self.number_of_channel = self.pattern_storage.shape[3]
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self.available_indices: np.ndarray = np.arange(self.number_of_pattern)
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self.on_epoch_end()
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def on_epoch_end(self) -> None:
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self.available_indices = np.arange(self.number_of_pattern)
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if self.do_shuffle is True:
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np.random.shuffle(self.available_indices)
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def __getitem__(self, index: int) -> tuple[np.ndarray, np.ndarray]:
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selected_indices: np.ndarray = self.available_indices[
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index * self.size_of_batch : (index + 1) * self.size_of_batch
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]
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image, target = self.__data_generation(selected_indices)
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return image, target
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def __data_generation(
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self, list_of_indice: np.ndarray
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) -> tuple[np.ndarray, np.ndarray]:
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image = np.empty(
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(self.size_of_batch, *self.dimensions, self.number_of_channel),
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dtype=np.float32,
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)
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target = np.empty((self.size_of_batch), dtype=int)
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for i in range(0, len(list_of_indice)):
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if self.pattern_storage.ndim == 3:
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image[i, :, :, 0] = self.pattern_storage[
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self.available_indices[list_of_indice[i]], :, :
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]
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else:
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image[i, :, :, :] = self.pattern_storage[
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self.available_indices[list_of_indice[i]], :, :, :
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]
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target[i] = self.label_storage[self.available_indices[list_of_indice[i]]]
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image = self.data_augmentation(image)
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return image, keras.utils.to_categorical(
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target, num_classes=self.number_of_classes
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)
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def __len__(self):
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return int(np.floor(self.number_of_pattern / self.size_of_batch))
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if __name__ == "__main__":
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pass
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```
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## Train an example MNIST network
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|[tf.keras.backend.clear_session](https://www.tensorflow.org/api_docs/python/tf/keras/backend/clear_session) | "Resets all state generated by Keras."|
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|[tf.keras.Sequential](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential) | "Sequential groups a linear stack of layers into a tf.keras.Model."|
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|[network.add()](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential#add) | "Adds a layer instance on top of the layer stack." |
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|[tf.keras.layers.Conv2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D) | "2D convolution layer (e.g. spatial convolution over images)."|
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|[tf.keras.layers.MaxPool2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPooling2D) | "Max pooling operation for 2D spatial data."|
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|[tf.keras.layers.Flatten](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Flatten) | "Flattens the input. Does not affect the batch size."|
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|[tf.keras.layers.Dense](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense) | "Just your regular densely-connected NN layer."|
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|[network.compile()](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential#compile) | "Configures the model for training."|
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|[tf.keras.metrics.categorical_crossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/categorical_crossentropy) | "Computes the categorical crossentropy loss."|
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|[tf.keras.optimizers.Adam](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) | "Optimizer that implements the Adam algorithm."|
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|[network.fit() ](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential#fit) | Trains the model for a fixed number of epochs (iterations on a dataset).|
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|[network.summary()](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential#summary) | "Prints a string summary of the network."|
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|[network.save()](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential#save) | "Saves the model to Tensorflow SavedModel or a single HDF5 file."|
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Parameters for the layers:
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[padding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1D): "One of "valid", "same" or "causal" (case-insensitive). "valid" means no padding. "same" results in padding with zeros evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. "causal" results in causal (dilated) convolutions, e.g. output[t] does not depend on input[t+1:]. "
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[use_bias](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1D): "Boolean, whether the layer uses a bias vector."
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[activation](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1D): "Activation function to use. If you don't specify anything, no activation is applied (see [keras.activations](https://www.tensorflow.org/api_docs/python/tf/keras/activations))."
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[data_format](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1D): " A string, one of channels_last (default) or channels_first."
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```python
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from tensorflow import keras
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from DataGenerator import DataGenerator
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epoch_max: int = 50
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number_of_classes: int = 10
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size_of_batch_train: int = 100
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train_data = DataGenerator(
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train=True,
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size_of_batch=size_of_batch_train,
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number_of_classes=number_of_classes,
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do_shuffle=True,
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)
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number_of_channels: int = train_data.number_of_channel
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input_dimensions = train_data.dimensions
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number_of_pattern_train = train_data.number_of_pattern
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number_of_output_channels_conv1: int = 32
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number_of_output_channels_conv2: int = 64
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number_of_neurons_flatten1: int = 1024
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kernel_size_conv1: tuple[int, int] = (5, 5)
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kernel_size_pool1: tuple[int, int] = (2, 2)
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kernel_size_conv2: tuple[int, int] = (5, 5)
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kernel_size_pool2: tuple[int, int] = (2, 2)
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stride_conv1: tuple[int, int] = (1, 1)
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stride_pool1: tuple[int, int] = (2, 2)
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stride_conv2: tuple[int, int] = (1, 1)
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stride_pool2: tuple[int, int] = (2, 2)
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keras.backend.clear_session()
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network = keras.Sequential()
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# Conv 1
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network.add(
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keras.layers.Conv2D(
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number_of_output_channels_conv1,
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kernel_size=kernel_size_conv1,
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activation="relu",
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input_shape=(input_dimensions[0], input_dimensions[1], number_of_channels),
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padding="valid",
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strides=stride_conv1,
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data_format="channels_last",
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use_bias=True,
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)
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)
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# Pool 1
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network.add(
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keras.layers.MaxPooling2D(
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pool_size=kernel_size_pool1,
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padding="valid",
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strides=stride_pool1,
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data_format="channels_last",
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)
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)
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# Conv 2
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network.add(
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keras.layers.Conv2D(
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number_of_output_channels_conv2,
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kernel_size=kernel_size_conv2,
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activation="relu",
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padding="valid",
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strides=stride_conv2,
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data_format="channels_last",
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use_bias=True,
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)
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)
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# Pool 2
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network.add(
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keras.layers.MaxPooling2D(
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pool_size=kernel_size_pool2,
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padding="valid",
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strides=stride_pool2,
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data_format="channels_last",
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)
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)
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# Flatten
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network.add(keras.layers.Flatten(data_format="channels_last"))
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# Full layer
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network.add(
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keras.layers.Dense(number_of_neurons_flatten1, activation="relu", use_bias=True)
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)
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# Output layer
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network.add(keras.layers.Dense(number_of_classes, activation="softmax"))
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network.compile(
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loss=keras.losses.categorical_crossentropy,
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optimizer=keras.optimizers.Adam(),
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metrics=["accuracy"],
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)
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for epoch_id in range(0, epoch_max):
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print(f"Epoch: {epoch_id} of {epoch_max - 1}")
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network.fit(x=train_data)
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network.summary()
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network.save("Model_" + str(epoch_id) + ".h5")
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```
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## Test the example network performance
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```python
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```
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