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‎README.md‎

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All methods mentioned below have their video and text tutorial in Chinese. Visit [莫烦 Python](https://morvanzhou.github.io/tutorials/) for more.
2020

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<!--
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* pyTorch basic
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* [torch and numpy](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/201_torch_numpy.py)
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* [Variable](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/202_variable.py)
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* [Activation](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/203_activation.py)
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* Tensorflow basic
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* [Session](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/201_session.py)
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* [Placeholder](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/202_placeholder.py)
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* [Variable](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/203_variable.py)
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* [Activation](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/204_activation.py)
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* Build your first network
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* [Regression](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/301_regression.py)
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* [Classification](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/302_classification.py)
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* [An easy way](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/303_build_nn_quickly.py)
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* [Save and reload](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/304_save_reload.py)
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* [Train on batch](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/305_batch_train.py)
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* [Optimizers](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/306_optimizer.py)
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* [Regression](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/301_simple_regression.py)
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* [Classification](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/302_simple_classification.py)
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* [Save and reload](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/303_save_reload.py)
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* [Optimizers](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/304_optimizer.py)
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* [Tensorboard](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/305_tensorboard.py)
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* Advanced neural network
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* [CNN](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/401_CNN.py)
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* [RNN-Classification](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/402_RNN_classifier.py)
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* [RNN-Regression](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/403_RNN_regressor.py)
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* [AutoEncoder](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/404_autoencoder.py)
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* [DQN Reinforcement Learning](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/405_DQN_Reinforcement_learning.py)
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* [CNN](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/401_CNN.py)
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* [RNN-Classification](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/402_RNN_classification.py)
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* [RNN-Regression](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/403_RNN_regression.py)
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* [AutoEncoder](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/404_AutoEncoder.py)
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* [DQN Reinforcement Learning](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/405_DQN_reinforcement_learning.py)
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* [GAN (Generative Adversarial Nets)](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/406_GAN.py) / [Conditional GAN](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/406_conditional_GAN.py)
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* Others (WIP)
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* [Why torch dynamic](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/501_why_torch_dynamic_graph.py)
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* [Train on GPU](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/502_GPU.py)
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* [Dropout](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/503_dropout.py)
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* [Batch Normalization](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/504_batch_normalization.py)
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* [Dropout](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/501_dropout.py)
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* [Batch Normalization](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/502_batch_normalization.py)
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### [Regression](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/301_regression.py)
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### [Regression](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/301_simple_regression.py)
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<a href="https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/301_regression.py">
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<a href="https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/301_simple_regression.py">
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<img class="course-image" src="https://morvanzhou.github.io/static/results/torch/1-1-2.gif">
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</a>
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### [Classification](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/302_classification.py)
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### [Classification](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/302_simple_classification.py)
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<a href="https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/302_classification.py">
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<a href="https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/302_simple_classification.py">
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<img class="course-image" src="https://morvanzhou.github.io/static/results/torch/1-1-3.gif">
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</a>
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### [RNN](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/403_RNN_regressor.py)
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### [RNN](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/403_RNN_regression.py)
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<a href="https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/403_RNN_regressor.py">
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<a href="https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/403_RNN_regression.py">
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<img class="course-image" src="https://morvanzhou.github.io/static/results/torch/4-3-1.gif" >
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</a>
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### [Autoencoder](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/404_autoencoder.py)
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### [Autoencoder](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/404_AutoEncoder.py)
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<a href="https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/403_RNN_regressor.py">
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<a href="https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/404_AutoEncoder.py">
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<img class="course-image" src="https://morvanzhou.github.io/static/results/torch/4-4-1.gif" >
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</a>
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<a href="https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/403_RNN_regressor.py">
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<a href="https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/404_AutoEncoder.py">
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<img class="course-image" src="https://morvanzhou.github.io/static/results/torch/4-4-2.gif" >
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</a>
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### [Dropout](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/503_dropout.py)
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<a href="https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/503_dropout.py">
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### [GAN (Generative Adversarial Nets)](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/406_GAN.py)
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<a href="https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/406_GAN.py">
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<img class="course-image" src="https://morvanzhou.github.io/static/results/torch/4-6-1.gif" >
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</a>
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### [Dropout](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/501_dropout.py)
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<a href="https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/501_dropout.py">
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<img class="course-image" src="https://morvanzhou.github.io/static/results/torch/5-3-1.gif" >
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</a>
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### [Batch Normalization](https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/504_batch_normalization.py)
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<a href="https://github.com/MorvanZhou/tutorials/blob/master/pytorchTUT/504_batch_normalization.py">
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### [Batch Normalization](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/502_batch_normalization.py)
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<a href="https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/502_batch_normalization.py">
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<img class="course-image" src="https://morvanzhou.github.io/static/results/torch/5-4-2.gif" >
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</a>
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-->
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# Donation
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tutorial-contents/session.py renamed to tutorial-contents/201_session.py

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tutorial-contents/placeholder.py renamed to tutorial-contents/202_placeholder.py

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Dependencies:
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tensorflow: 1.1.0
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matplotlib
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numpy
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"""
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import tensorflow as tf
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‎tutorial-contents/203_variable.py‎

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"""
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Know more, visit my Python tutorial page: https://morvanzhou.github.io/tutorials/
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My Youtube Channel: https://www.youtube.com/user/MorvanZhou
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Dependencies:
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tensorflow: 1.1.0
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"""
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import tensorflow as tf
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var = tf.Variable(0) # our first variable in the "global_variable" set
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add_operation = tf.add(var, 1)
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update_operation = tf.assign(var, add_operation)
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with tf.Session() as sess:
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# once define variables, you have to initialize them by doing this
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sess.run(tf.global_variables_initializer())
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for _ in range(3):
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sess.run(update_operation)
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print(sess.run(var))

‎tutorial-contents/204_activation.py‎

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"""
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Know more, visit my Python tutorial page: https://morvanzhou.github.io/tutorials/
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My Youtube Channel: https://www.youtube.com/user/MorvanZhou
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Dependencies:
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tensorflow: 1.1.0
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matplotlib
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"""
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import tensorflow as tf
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import numpy as np
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import matplotlib.pyplot as plt
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# fake data
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x = np.linspace(-5, 5, 200) # x data, shape=(100, 1)
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# following are popular activation functions
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y_relu = tf.nn.relu(x)
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y_sigmoid = tf.nn.sigmoid(x)
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y_tanh = tf.nn.tanh(x)
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y_softplus = tf.nn.softplus(x)
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# y_softmax = tf.nn.softmax(x) softmax is a special kind of activation function, it is about probability
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sess = tf.Session()
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y_relu, y_sigmoid, y_tanh, y_softplus = sess.run([y_relu, y_sigmoid, y_tanh, y_softplus])
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# plt to visualize these activation function
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plt.figure(1, figsize=(8, 6))
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plt.subplot(221)
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plt.plot(x, y_relu, c='red', label='relu')
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plt.ylim((-1, 5))
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plt.legend(loc='best')
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plt.subplot(222)
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plt.plot(x, y_sigmoid, c='red', label='sigmoid')
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plt.ylim((-0.2, 1.2))
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plt.legend(loc='best')
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plt.subplot(223)
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plt.plot(x, y_tanh, c='red', label='tanh')
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plt.ylim((-1.2, 1.2))
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plt.legend(loc='best')
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plt.subplot(224)
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plt.plot(x, y_softplus, c='red', label='softplus')
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plt.ylim((-0.2, 6))
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plt.legend(loc='best')
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plt.show()

‎tutorial-contents/301_simple_regression.py‎

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plt.scatter(x, y)
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plt.show()
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tf_x = tf.placeholder(tf.float32, x.shape, 'x') # input x
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tf_y = tf.placeholder(tf.float32, y.shape, 'y') # input y
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tf_x = tf.placeholder(tf.float32, x.shape) # input x
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tf_y = tf.placeholder(tf.float32, y.shape) # input y
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# neural network layers
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l1 = tf.layers.dense(tf_x, 10, tf.nn.relu) # hidden layer

‎tutorial-contents/302_simple_classification.py‎

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plt.scatter(x[:, 0], x[:, 1], c=y, s=100, lw=0, cmap='RdYlGn')
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plt.show()
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tf_x = tf.placeholder(tf.float32, x.shape, 'x') # input x
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tf_y = tf.placeholder(tf.int32, y.shape, 'y') # input y
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tf_x = tf.placeholder(tf.float32, x.shape) # input x
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tf_y = tf.placeholder(tf.int32, y.shape) # input y
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# neural network layers
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l1 = tf.layers.dense(tf_x, 10, tf.nn.relu) # hidden layer

‎tutorial-contents/303_save_reload.py‎

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"""
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Know more, visit my Python tutorial page: https://morvanzhou.github.io/tutorials/
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My Youtube Channel: https://www.youtube.com/user/MorvanZhou
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Dependencies:
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tensorflow: 1.1.0
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matplotlib
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numpy
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"""
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import tensorflow as tf
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import matplotlib.pyplot as plt
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import numpy as np
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tf.set_random_seed(1)
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np.random.seed(1)
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# fake data
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x = np.linspace(-1, 1, 100)[:, np.newaxis] # shape (100, 1)
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noise = np.random.normal(0, 0.1, size=x.shape)
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y = np.power(x, 2) + noise # shape (100, 1) + some noise
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def save():
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print('This is save')
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# build neural network
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tf_x = tf.placeholder(tf.float32, x.shape) # input x
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tf_y = tf.placeholder(tf.float32, y.shape) # input y
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l = tf.layers.dense(tf_x, 10, tf.nn.relu) # hidden layer
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o = tf.layers.dense(l, 1) # output layer
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loss = tf.losses.mean_squared_error(tf_y, o) # compute cost
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train_op = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(loss)
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sess = tf.Session()
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sess.run(tf.global_variables_initializer()) # initialize var in graph
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saver = tf.train.Saver() # define a saver for saving and restoring
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for step in range(100): # train
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sess.run(train_op, {tf_x: x, tf_y: y})
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saver.save(sess, 'params', write_meta_graph=False) # meta_graph is not recommended
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# plotting
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pred, l = sess.run([o, loss], {tf_x: x, tf_y: y})
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plt.figure(1, figsize=(10, 5))
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plt.subplot(121)
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plt.scatter(x, y)
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plt.plot(x, pred, 'r-', lw=5)
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plt.text(-1, 1.2, 'Save Loss=%.4f' % l, fontdict={'size': 15, 'color': 'red'})
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def reload():
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print('This is reload')
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# build entire net again and restore
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tf_x = tf.placeholder(tf.float32, x.shape) # input x
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tf_y = tf.placeholder(tf.float32, y.shape) # input y
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l_ = tf.layers.dense(tf_x, 10, tf.nn.relu) # hidden layer
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o_ = tf.layers.dense(l_, 1) # output layer
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loss_ = tf.losses.mean_squared_error(tf_y, o_) # compute cost
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sess = tf.Session()
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# don't need to initialize variables, just restoring trained variables
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saver = tf.train.Saver() # define a saver for saving and restoring
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saver.restore(sess, 'params')
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# plotting
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pred, l = sess.run([o_, loss_], {tf_x: x, tf_y: y})
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plt.subplot(122)
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plt.scatter(x, y)
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plt.plot(x, pred, 'r-', lw=5)
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plt.text(-1, 1.2, 'Reload Loss=%.4f' % l, fontdict={'size': 15, 'color': 'red'})
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plt.show()
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save()
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# destroy previous net
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tf.reset_default_graph()
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reload()

‎tutorial-contents/304_optimizer.py‎

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"""
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Know more, visit my Python tutorial page: https://morvanzhou.github.io/tutorials/
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My Youtube Channel: https://www.youtube.com/user/MorvanZhou
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Dependencies:
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tensorflow: 1.1.0
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matplotlib
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numpy
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"""
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import tensorflow as tf
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import matplotlib.pyplot as plt
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import numpy as np
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tf.set_random_seed(1)
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np.random.seed(1)
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LR = 0.01
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BATCH_SIZE = 32
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# fake data
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x = np.linspace(-1, 1, 100)[:, np.newaxis] # shape (100, 1)
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noise = np.random.normal(0, 0.1, size=x.shape)
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y = np.power(x, 2) + noise # shape (100, 1) + some noise
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# plot dataset
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plt.scatter(x, y)
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plt.show()
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# default network
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class Net:
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def __init__(self, opt, **kwargs):
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self.x = tf.placeholder(tf.float32, [None, 1])
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self.y = tf.placeholder(tf.float32, [None, 1])
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l = tf.layers.dense(self.x, 20, tf.nn.relu)
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out = tf.layers.dense(l, 1)
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self.loss = tf.losses.mean_squared_error(self.y, out)
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self.train = opt(LR, **kwargs).minimize(self.loss)
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# different nets
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net_SGD = Net(tf.train.GradientDescentOptimizer)
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net_Momentum = Net(tf.train.MomentumOptimizer, momentum=0.9)
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net_RMSprop = Net(tf.train.RMSPropOptimizer)
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net_Adam = Net(tf.train.AdamOptimizer)
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nets = [net_SGD, net_Momentum, net_RMSprop, net_Adam]
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sess = tf.Session()
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sess.run(tf.global_variables_initializer())
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losses_his = [[], [], [], []] # record loss
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# training
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for step in range(300): # for each training step
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index = np.random.randint(0, x.shape[0], BATCH_SIZE)
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b_x = x[index]
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b_y = y[index]
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for net, l_his in zip(nets, losses_his):
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_, l = sess.run([net.train, net.loss], {net.x: b_x, net.y: b_y})
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l_his.append(l) # loss recoder
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# plot loss history
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labels = ['SGD', 'Momentum', 'RMSprop', 'Adam']
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for i, l_his in enumerate(losses_his):
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plt.plot(l_his, label=labels[i])
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plt.legend(loc='best')
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plt.xlabel('Steps')
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plt.ylabel('Loss')
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plt.ylim((0, 0.2))
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plt.show()

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