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1-Introduction/.ipynb_checkpoints/basic_operations-checkpoint.ipynb
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,171 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import tensorflow as tf" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "tf.Tensor(\n", | ||
| "[[1. 1. 1.]\n", | ||
| " [1. 1. 1.]], shape=(2, 3), dtype=float32)\n", | ||
| "tf.Tensor(\n", | ||
| "[[10. 1. 1.]\n", | ||
| " [ 1. 1. 1.]], shape=(2, 3), dtype=float32)\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "###======================================= assign value ===================================#\n", | ||
| "\n", | ||
| "a = tf.ones([2,3])\n", | ||
| "print(a)\n", | ||
| "\n", | ||
| "# a[0,0] = 10 => TypeError: 'tensorflow.python.framework.ops.EagerTensor' object does not support item assignment\n", | ||
| "\n", | ||
| "a = tf.Variable(a)\n", | ||
| "a[0,0].assign(10)\n", | ||
| "b = a.read_value()\n", | ||
| "print(b)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "a + b : 5\n", | ||
| "Addition with constants: tf.Tensor(5, shape=(), dtype=int32)\n", | ||
| "Addition with constants: tf.Tensor(5, shape=(), dtype=int32)\n", | ||
| "a * b : 6\n", | ||
| "Multiplication with constants: tf.Tensor(6, shape=(), dtype=int32)\n", | ||
| "Multiplication with constants: tf.Tensor(6, shape=(), dtype=int32)\n", | ||
| "Multiplication with matrixes: tf.Tensor([[12.]], shape=(1, 1), dtype=float32)\n", | ||
| "broadcast matrix in Multiplication: tf.Tensor(\n", | ||
| "[[6. 6.]\n", | ||
| " [6. 6.]], shape=(2, 2), dtype=float32)\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "###======================================= add, multiply, div. etc ===================================#\n", | ||
| "\n", | ||
| "a = tf.constant(2)\n", | ||
| "b = tf.constant(3)\n", | ||
| "\n", | ||
| "print(\"a + b :\" , a.numpy() + b.numpy())\n", | ||
| "print(\"Addition with constants: \", a+b)\n", | ||
| "print(\"Addition with constants: \", tf.add(a, b))\n", | ||
| "print(\"a * b :\" , a.numpy() * b.numpy())\n", | ||
| "print(\"Multiplication with constants: \", a*b)\n", | ||
| "print(\"Multiplication with constants: \", tf.multiply(a, b))\n", | ||
| "\n", | ||
| "\n", | ||
| "# ----------------\n", | ||
| "# More in details:\n", | ||
| "# Matrix Multiplication from TensorFlow official tutorial\n", | ||
| "\n", | ||
| "# Create a Constant op that produces a 1x2 matrix. The op is\n", | ||
| "# added as a node to the default graph.\n", | ||
| "#\n", | ||
| "# The value returned by the constructor represents the output\n", | ||
| "# of the Constant op.\n", | ||
| "matrix1 = tf.constant([[3., 3.]])\n", | ||
| "\n", | ||
| "# Create another Constant that produces a 2x1 matrix.\n", | ||
| "matrix2 = tf.constant([[2.],[2.]])\n", | ||
| "\n", | ||
| "# Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs.\n", | ||
| "# The returned value, 'product', represents the result of the matrix\n", | ||
| "# multiplication.\n", | ||
| "product = tf.matmul(matrix1, matrix2)\n", | ||
| "print(\"Multiplication with matrixes:\", product)\n", | ||
| "\n", | ||
| "# broadcast matrix in Multiplication\n", | ||
| "\n", | ||
| "print(\"broadcast matrix in Multiplication:\", matrix1 * matrix2)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "tf.Tensor(2.0, shape=(), dtype=float32) tf.Tensor(2, shape=(), dtype=int32)\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "###===================================== cast operations =====================================#\n", | ||
| "\n", | ||
| "a = tf.convert_to_tensor(2.)\n", | ||
| "b = tf.cast(a, tf.int32)\n", | ||
| "print(a, b)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "2 3\n", | ||
| "tf.Tensor(2, shape=(), dtype=int32) tf.Tensor(3, shape=(), dtype=int32)\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "###===================================== shape operations ===================================#\n", | ||
| "\n", | ||
| "a = tf.ones([2,3])\n", | ||
| "print(a.shape[0], a.shape[1]) # 2, 3\n", | ||
| "shape = tf.shape(a) # a tensor\n", | ||
| "print(shape[0], shape[1])" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.5.2" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,171 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import tensorflow as tf" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "tf.Tensor(\n", | ||
| "[[1. 1. 1.]\n", | ||
| " [1. 1. 1.]], shape=(2, 3), dtype=float32)\n", | ||
| "tf.Tensor(\n", | ||
| "[[10. 1. 1.]\n", | ||
| " [ 1. 1. 1.]], shape=(2, 3), dtype=float32)\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "###======================================= assign value ===================================#\n", | ||
| "\n", | ||
| "a = tf.ones([2,3])\n", | ||
| "print(a)\n", | ||
| "\n", | ||
| "# a[0,0] = 10 => TypeError: 'tensorflow.python.framework.ops.EagerTensor' object does not support item assignment\n", | ||
| "\n", | ||
| "a = tf.Variable(a)\n", | ||
| "a[0,0].assign(10)\n", | ||
| "b = a.read_value()\n", | ||
| "print(b)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "a + b : 5\n", | ||
| "Addition with constants: tf.Tensor(5, shape=(), dtype=int32)\n", | ||
| "Addition with constants: tf.Tensor(5, shape=(), dtype=int32)\n", | ||
| "a * b : 6\n", | ||
| "Multiplication with constants: tf.Tensor(6, shape=(), dtype=int32)\n", | ||
| "Multiplication with constants: tf.Tensor(6, shape=(), dtype=int32)\n", | ||
| "Multiplication with matrixes: tf.Tensor([[12.]], shape=(1, 1), dtype=float32)\n", | ||
| "broadcast matrix in Multiplication: tf.Tensor(\n", | ||
| "[[6. 6.]\n", | ||
| " [6. 6.]], shape=(2, 2), dtype=float32)\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "###======================================= add, multiply, div. etc ===================================#\n", | ||
| "\n", | ||
| "a = tf.constant(2)\n", | ||
| "b = tf.constant(3)\n", | ||
| "\n", | ||
| "print(\"a + b :\" , a.numpy() + b.numpy())\n", | ||
| "print(\"Addition with constants: \", a+b)\n", | ||
| "print(\"Addition with constants: \", tf.add(a, b))\n", | ||
| "print(\"a * b :\" , a.numpy() * b.numpy())\n", | ||
| "print(\"Multiplication with constants: \", a*b)\n", | ||
| "print(\"Multiplication with constants: \", tf.multiply(a, b))\n", | ||
| "\n", | ||
| "\n", | ||
| "# ----------------\n", | ||
| "# More in details:\n", | ||
| "# Matrix Multiplication from TensorFlow official tutorial\n", | ||
| "\n", | ||
| "# Create a Constant op that produces a 1x2 matrix. The op is\n", | ||
| "# added as a node to the default graph.\n", | ||
| "#\n", | ||
| "# The value returned by the constructor represents the output\n", | ||
| "# of the Constant op.\n", | ||
| "matrix1 = tf.constant([[3., 3.]])\n", | ||
| "\n", | ||
| "# Create another Constant that produces a 2x1 matrix.\n", | ||
| "matrix2 = tf.constant([[2.],[2.]])\n", | ||
| "\n", | ||
| "# Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs.\n", | ||
| "# The returned value, 'product', represents the result of the matrix\n", | ||
| "# multiplication.\n", | ||
| "product = tf.matmul(matrix1, matrix2)\n", | ||
| "print(\"Multiplication with matrixes:\", product)\n", | ||
| "\n", | ||
| "# broadcast matrix in Multiplication\n", | ||
| "\n", | ||
| "print(\"broadcast matrix in Multiplication:\", matrix1 * matrix2)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "tf.Tensor(2.0, shape=(), dtype=float32) tf.Tensor(2, shape=(), dtype=int32)\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "###===================================== cast operations =====================================#\n", | ||
| "\n", | ||
| "a = tf.convert_to_tensor(2.)\n", | ||
| "b = tf.cast(a, tf.int32)\n", | ||
| "print(a, b)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "2 3\n", | ||
| "tf.Tensor(2, shape=(), dtype=int32) tf.Tensor(3, shape=(), dtype=int32)\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "###===================================== shape operations ===================================#\n", | ||
| "\n", | ||
| "a = tf.ones([2,3])\n", | ||
| "print(a.shape[0], a.shape[1]) # 2, 3\n", | ||
| "shape = tf.shape(a) # a tensor\n", | ||
| "print(shape[0], shape[1])" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.5.2" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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