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nd1511
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Python, PyCharm IDE
Python, PyCharm IDE
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‎program45_SGD_GANs.py

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from __future__ import print_function
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from __future__ import absolute_import
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# use: https://github.com/tensorflow/docs/blob/master/site/en/r1/tutorials/sequences/audio_recognition.md
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# https://github.com/tensorflow/docs/blob/master/site/en/r1/tutorials/sequences/audio_recognition.md
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# we now use: https://www.youtube.com/watch?v=9dXiAecyJrY&feature=youtu.be&t=13874
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# https://medium.com/startup-grind/fueling-the-ai-gold-rush-7ae438505bc2
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# https://www.analyticsinsight.net/best-computer-vision-courses-to-master-in-2019/
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# UCI data: https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
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# Human Activity Recognition Using Smartphones Data Set, archive.ics.uci.edu, Human Activity Recognition
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# https://www.cfasociety.org/cleveland/Lists/Events%20Calendar/Attachments/1045/BIG-Data_AI-JPMmay2017.pdf
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import numpy as np
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import numpy.random
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import pandas as pd
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import sys, sklearn
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import numpy.random
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import sys, sklearn
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import scipy.stats as ss
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import os, tarfile, errno
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import matplotlib.pyplot as plt
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# https://www.cmegroup.com/education/files/jpm-momentum-strategies-2015-04-15-1681565.pdf
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# https://faculty.sites.uci.edu/pjorion/files/2018/05/JPM-2017-MachineLearningInvestments.pdf
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# we use: https://www.cmegroup.com/education/files/jpm-momentum-strategies-2015-04-15-1681565.pdf
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# https://www.cfasociety.org/cleveland/Lists/Events%20Calendar/Attachments/1045/BIG-Data_AI-JPMmay2017.pdf
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import tensorflow as tf
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print(tf.__version__) # 1.14.0
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import matplotlib.pyplot as plt
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import seaborn as sns; sns.set()
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# https://medium.com/startup-grind/fueling-the-ai-gold-rush-7ae438505bc2
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# https://www.analyticsinsight.net/best-computer-vision-courses-to-master-in-2019/
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# UCI data: https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
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# Human Activity Recognition Using Smartphones Data Set, archive.ics.uci.edu, Human Activity Recognition
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# https://www.cfasociety.org/cleveland/Lists/Events%20Calendar/Attachments/1045/BIG-Data_AI-JPMmay2017.pdf
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# use: https://github.com/tensorflow/docs/blob/master/site/en/r1/tutorials/sequences/audio_recognition.md
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# https://github.com/tensorflow/docs/blob/master/site/en/r1/tutorials/sequences/audio_recognition.md
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# we now use: https://www.youtube.com/watch?v=9dXiAecyJrY&feature=youtu.be&t=13874
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if sys.version_info >= (3, 0, 0):
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import urllib.request as urllib
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from matplotlib import pyplot as plt
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import torchvision.transforms as transforms
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#import renom as rm
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from copy import deepcopy
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import matplotlib.pyplot as plt
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import numpy as np
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#import renom as rm
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import tensorflow as tf
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from copy import deepcopy
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from keras.layers import Dense
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import matplotlib.pyplot as plt
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from keras.datasets import mnist
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from keras.models import Sequential
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# https://www.renom.jp/notebooks/tutorial/generative-model/anoGAN/notebook.html
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# use: https://www.renom.jp/notebooks/tutorial/generative-model/anoGAN/notebook.html
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import external.renom as rm
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import externals.renom as rm
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#from renom.optimizer import Adam
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#from renom.cuda import set_cuda_active
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from external.renom.optimizer import Adam
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from external.renom.cuda import set_cuda_active
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from externals.renom.optimizer import Adam
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from externals.renom.cuda import set_cuda_active
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# MNIST: Keras or scikit-learn embedded datasets
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# Keras: from keras.datasets import mnist
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return y4
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# Backward propagation
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def backward(self, X, l, y4):
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def backward(self, X, l, y4): # Back, Backward
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# Derivative of binary cross entropy cost w.r.t. final output y4
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self.dC_dy4 = y4 - l
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idx = np.random.randint(0, X_train.shape[0], half_batch)
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imgs = X_train[idx]
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# Generator => Generate a half batch of new images
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noise = np.random.normal(0, 1, (half_batch, 100))
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# Generate a half batch of new images
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gen_imgs = self.generator.predict(noise)
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# Train the Discriminator
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# we use: https://machinelearningmastery.com/how-to-develop-a-generative-adversarial-network-for-a-cifar-10-small-object-photographs-from-scratch/
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(trainX, _), (_, _) = load_data() # load the CIFAR-10 dataset
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X = trainX.astype('float32') # convert from unsigned ints to floats
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# scale from [0,255] to [-1,1]
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X = (X - 127.5) / 127.5
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X = trainX.astype('float32') # convert from ints to floats
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X = (X - 127.5) / 127.5 # scale from [0,255] to [-1,1]
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# load and prepare CIFAR-10 training images
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def load_real_samples():
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# ReLU is the most common activation function. We use ReLU.
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# Mean across a batch. We use batches. Normalize across a batch.
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# https://github.com/life-efficient/Academy-of-AI/blob/master/Lecture%2013%20-%20Generative%20Models/GANs%20tutorial.ipynb
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# use: https://github.com/life-efficient/Academy-of-AI/blob/master/Lecture%2013%20-%20Generative%20Models/GANs%20tutorial.ipynb
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import torch
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import torchvision
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