data augmentation

Author: astorfi

codes/python/0-welcome/README.rst

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+# -*- coding: utf-8 -*-
+"""welcome.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/github/instillai/TensorFlow-Course/blob/master/codes/ipython/0-welcome/welcome.ipynb
+"""
+
+# Import tensorflow
+import tensorflow as tf
+
+# Check version
+print("Tensorflow version: ", tf.__version__)
+
+# Test TensorFlow for cuda availibility
+print("Tensorflow is built with CUDA: ", tf.test.is_built_with_cuda())
+
+# Check devices
+print("All devices: ", tf.config.list_physical_devices(device_type=None))
+print("GPU devices: ", tf.config.list_physical_devices(device_type='GPU'))
+
+# Print a randomly generated tensor
+# tf.math.reduce_sum: https://www.tensorflow.org/api_docs/python/tf/math/reduce_sum
+# tf.random.normal: https://www.tensorflow.org/api_docs/python/tf/random/normal
+print(tf.math.reduce_sum(tf.random.normal([1, 10])))
+

codes/python/0-welcome/code/0-welcome.py

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+# -*- coding: utf-8 -*-
+"""tensors.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/github/instillai/TensorFlow-Course/blob/master/codes/ipython/1-basics/tensors.ipynb
+"""
+
+# Import necessary libraries
+import tensorflow as tf
+import numpy as np
+
+"""## Tensors
+
+Tensor are multi-dimensitonal arrays that are used in Tensorflow.
+
+We use the following definition:
+
+* **Rank:** The number of dimensions that a vector has.
+
+Below, we will define different kinds of tensors and show their rank using [tf.rank](https://www.tensorflow.org/api_docs/python/tf/rank) function.
+"""
+
+tensor = tf.constant(0)
+print("Print constant tensor {} of rank {}".format(tensor, tf.rank(tensor)))
+print("Show full tensor:", tensor)
+
+# NOTE: We use .numpy() to transform tf.tensor to numpy
+tensor = tf.constant([1,2,3])
+print("Tensor:", tensor)
+print("Rank:", tf.rank(tensor).numpy())
+
+"""### Tensor Operations"""
+
+x = tf.constant([[1, 1],
+                 [1, 1]])
+y = tf.constant([[2, 4],
+                 [6, 8]])
+
+# Add two tensors
+print(tf.add(x, y), "\n")
+
+# Add two tensors
+print(tf.matmul(x, y), "\n")
+
+"""### Muti-dimentional Tensors
+
+This part is not much different compared to what we learned so far. However, it would be nice to try extracting as much information as we can from a multi-dimentional tensor.
+
+
+Let's use [tf.ones](https://www.tensorflow.org/api_docs/python/tf/ones) for our purpose here. It creates an all-one tensor.
+"""
+
+# We set the shape of the tensor and the desired data type.
+tensor = tf.ones(shape = [2, 3, 6], dtype = tf.float32)
+print('Tensor:', tensor)
+
+print("Tensor Rank: ", tf.rank(tensor).numpy())
+print("Shape: ", tensor.shape)
+print("Elements' type", tensor.dtype)
+print("The size of the second axis:", tensor.shape[1])
+print("The size of the last axis:", tensor.shape[-1])
+print("Total number of elements: ", tf.size(tensor).numpy())
+print("How many dimensions? ", tensor.ndim)
+
+"""### Indexing
+
+TensorFlow indexing is aligned with Python indexing. See the following examples.
+"""
+
+x = tf.constant([[1, 2, 3],
+                 [4, 5, 6],
+                 [7, 8, 9]])
+
+# All elements
+print(x[:].numpy())
+
+# All elements of the first row
+print(x[0,:].numpy())
+
+# First row and last column
+print(x[0,-1].numpy())
+
+# From second row to last and from third column to last
+print(x[1:,2:].numpy)
+
+"""### Data types
+
+You can change the data type of the tesnorflow tensors for your purpose. This will be done easily by [tf.cast](https://www.tensorflow.org/api_docs/python/tf/cast).
+"""
+
+original_tensor = tf.constant([1, 2, 3, 4], dtype=tf.int32)
+print('Original tensor: ', original_tensor)
+print("Tensor type before casting: ", original_tensor.dtype)
+
+# Casting to change dtype
+casted_tensor = tf.cast(original_tensor, dtype=tf.float32)
+print('New tensor: ', casted_tensor)
+print("Tensor type after casting: ", casted_tensor.dtype)
+