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## an-inquiry-into-matplotlib-figures.ipynb

      
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                akashpalrecha
                / an-inquiry-into-matplotlib-figures.ipynb
            
            
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              January 13, 2023 16:32
            
              
                An Inquiry into Matplotlib's Figures, Axes, subplots and the very amazing GridSpec!
              
          
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## data_loader.py
"""
Create train, valid, test iterators for CIFAR-10 [1].
Easily extended to MNIST, CIFAR-100 and Imagenet.

[1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4
"""

import torch
import numpy as np

## neural11lines.py
X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ])
y = np.array([[0,1,1,0]]).T
syn0 = 2*np.random.random((3,4)) - 1
syn1 = 2*np.random.random((4,1)) - 1
for j in xrange(60000):
    l1 = 1/(1+np.exp(-(np.dot(X,syn0))))
    l2 = 1/(1+np.exp(-(np.dot(l1,syn1))))
    l2_delta = (y - l2)*(l2*(1-l2))
    l1_delta = l2_delta.dot(syn1.T) * (l1 * (1-l1))
    syn1 += l1.T.dot(l2_delta)

## min-char-rnn.py
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)
	"""
	Create train, valid, test iterators for CIFAR-10 [1].
	Easily extended to MNIST, CIFAR-100 and Imagenet.

	[1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4
	"""

	import torch
	import numpy as np
	X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ])
	y = np.array([[0,1,1,0]]).T
	syn0 = 2*np.random.random((3,4)) - 1
	syn1 = 2*np.random.random((4,1)) - 1
	for j in xrange(60000):
	l1 = 1/(1+np.exp(-(np.dot(X,syn0))))
	l2 = 1/(1+np.exp(-(np.dot(l1,syn1))))
	l2_delta = (y - l2)(l2(1-l2))
	l1_delta = l2_delta.dot(syn1.T) * (l1 * (1-l1))
	syn1 += l1.T.dot(l2_delta)
	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)