def data_mnist(datadir='/tmp/', train_start=0, train_end=60000, test_start=0,
test_end=10000):
"""
Load and preprocess MNIST dataset
:param datadir: path to folder where data should be stored
:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:return: tuple of four arrays containing training data, training labels,
testing data and testing labels.
"""
assert isinstance(train_start, int)
assert isinstance(train_end, int)
assert isinstance(test_start, int)
assert isinstance(test_end, int)
import mnist
X_train = mnist.train_images() / 255.
Y_train = mnist.train_labels()
X_test = mnist.test_images() / 255.
Y_test = mnist.test_labels()
X_train = np.expand_dims(X_train, -1)
X_test = np.expand_dims(X_test, -1)
X_train = X_train[train_start:train_end]
Y_train = Y_train[train_start:train_end]
X_test = X_test[test_start:test_end]
Y_test = Y_test[test_start:test_end]
Y_train = utils.to_categorical(Y_train, num_classes=10)
Y_test = utils.to_categorical(Y_test, num_classes=10)
return X_train, Y_train, X_test, Y_test
#written by jorge orlando miranda ñahui #import the neccesary modules import numpy as np import mnist from keras.models import Sequential from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten from keras.utils import to_categorical #load the data from the mnits data set #training images (60000 records) train_images = mnist.train_images() train_labels = mnist.train_labels() #testing images (10000 records) test_images = mnist.test_images() test_labels = mnist.test_labels() #normalize the values to the range of [-0.5 0.5] norm_train = train_images / 255 - 0.5 test_images = test_images / 255 - 0.5 norm_train = np.expand_dims(norm_train, axis=3) test_images = np.expand_dims(test_images, axis=3) CNN_model = Sequential() #number of filters (9) num_filters = 9 #size of each filter (3x3) filter_size = 3 #dimension of input 28x28x1 (grayscale image) shape_input = (28, 28, 1) CNN_model.add(Conv2D(num_filters, filter_size, input_shape=shape_input)) #size of the pooling layer (2x2) size_pool = 2 #max Polling layer CNN_model.add(MaxPooling2D(pool_size=size_pool))
import mnist
import numpy as np
from conv import Conv
from maxpool import MaxPool
from softmax import Softmax
import cv2
'''
input_img = np.array([[0,0,0,0,0,0], [0,0,50,0,29,0],
[0,0,80,31,2,0], [0,33,90,0,75,0],
[0,0,9,0,95,0], [0,0,0,0,0,0]
])
'''
train_images = mnist.train_images()[:1000]
train_labels = mnist.train_labels()[:1000]
test_images = mnist.test_images()[:1000] # test image size: 28 x 28
test_labels = mnist.test_labels()[:1000]
conv = Conv(8) # 28 x 28 -> 26 x 26 x 8
pool = MaxPool() # 26 x 26 x 8 -> 13 x 13 x 8
softmax = Softmax(13*13*8,10) # 10 nodes for 10 digits 0 -> 9
def forward(images,labels):
# transform image from [0->255] to [-0.5->0.5]
out = conv.forward((images / 255)-0.5)
out = pool.forward(out)
out = softmax.forward(out)
loss = -np.log(out[labels])
if np.argmax(out) == labels:
import mnist # pip install mnist
import numpy as np
import matplotlib.pyplot as plt
from NeuralNetwork import NeuralNetwork
from activations import relu, d_relu, softmax, d_softmax
from loss_functions import categorical_crossentropy, d_categorical_crossentropy
from optimizers import SGD
x_train, y_train = mnist.train_images(), mnist.train_labels()
n_features = x_train.shape[1] * x_train.shape[2]
x_train_flatten = x_train.reshape(
(x_train.shape[0], n_features)).astype(np.float64)
x_test, y_test = mnist.test_images(), mnist.test_labels()
x_test_flatten = x_test.reshape(
(x_test.shape[0], n_features)).astype(np.float64)
nn_mnist = NeuralNetwork(layers=[n_features, 100, 10],
hidden_activation=(relu, d_relu),
output_activation=(softmax, d_softmax),
loss=(categorical_crossentropy,
d_categorical_crossentropy),
optimizer=SGD())
mnist_loss_hist = nn_mnist.fit(x=x_train_flatten,
y=y_train,
batch_size=512,
epochs=100,
import numpy as np
import mnist as mn
import matplotlib.pyplot as plt
# SREDJIVANJE PROMENLJIVIH
eig_vec = np.genfromtxt('eigvec.csv', delimiter=',')
#-------------
images = mn.train_images()
images = images.reshape(60000, 784)
#images1 = images1.reshape(60000,784)
#images2 = mn.test_images()
#images2 = images2.reshape(10000,784)
#images = np.vstack((images1, images2))
images = np.dot(eig_vec.T, images.T)
images = images[:, :
100] # menjas ovo na sta hoces, ali moras da promenis i label na isti br
#images = images.reshape(5,10)
images = images.T
images = images / np.std(images)
for i in range(images.shape[0]):
j = 0
for j in range(images.shape[1]):
if images[i][j] > 0:
images[i][j] = 1
elif images[i][j] >= -0.6 and images[i][j] <= 0.6:
images[i][j] = 0
elif images[i][j] < 0:
import mnist
import numpy as np
import tensorflow as tf
import tensorflow.distributions as tfds
import matplotlib.pyplot as plt
from vptsne import (VAE, PTSNE, VPTSNE)
from vptsne.helpers import *
from common import *
from sklearn.decomposition import PCA
from sklearn.manifold.t_sne import trustworthiness
from sklearn.neighbors import KNeighborsClassifier as KNC
np.random.seed(0)
color_palette = np.random.rand(100, 3)
mnist_train_images, mnist_train_labels = mnist.train_images().reshape(60000, 784) / 255, mnist.train_labels()
mnist_test_images, mnist_test_labels = mnist.test_images().reshape(10000, 784) / 255, mnist.test_labels()
n_input_dimensions = mnist_train_images.shape[1]
def run_training(n_latent_dimensions, perplexity, batch_size, percent_missing):
data_points = mnist_train_images.shape[0]
indices = np.random.choice(data_points, int(data_points * (1 - percent_missing)), replace=False)
train_data = mnist_train_images[indices]
train_labels = mnist_train_labels[indices]
test_data = mnist_test_images
test_labels = mnist_test_labels
vae = VAE(
[n_input_dimensions],
get_gaussian_network_builder(vae_encoder_layers, n_latent_dimensions),
gaussian_prior_supplier,
import numpy as np
import mnist as mn
# SREDJIVANJE PROMENLJIVIH
eig_vec = np.genfromtxt('eig_vec.csv', delimiter=',')
#-------------
images1 = mn.train_images()
images1 = images1.reshape(60000, 784)
images2 = mn.test_images()
images2 = images2.reshape(10000, 784)
images = np.vstack((images1, images2))
images = np.dot(eig_vec.T, images.T)
images = images[:, :
60000] # menjas ovo na sta hoces, ali moras da promenis i label na isti br
#images = images.reshape(5,10)
images = images.T
images = images / np.std(images)
for i in range(images.shape[0]):
j = 0
for j in range(images.shape[1]):
if images[i][j] > 0.5:
images[i][j] = 1
elif images[i][j] >= -0.5 and images[i][j] <= 0.5:
images[i][j] = 0
elif images[i][j] < -0.5:
images[i][j] = -1
def training_data():
load_data = [train_images(), train_labels()]
training_inputs = [np.reshape(x, (784, 1)) for x in load_data[0]]
training_results = [vectorized_result(y) for y in load_data[1]]
train = zip(training_inputs, training_results)
return train
i+=1
err_best_g_prev = err_best_g
t_new = time.time()
if verbose: print('iter: {}, best solution: {} time elapsed in secs:{} Tot: {}'.format(i,err_best_g,float(t_new-t_old),float(t_new-t_init)))
f.write(str(float(err_best_g)) + '\n')
print('\nFINAL SOLUTION:')
#print(' > {}'.format(self.pos_best_g))
print(' > {}\n'.format(err_best_g))
t_total_new = time.time()
print('total time elapsed:{}secs'.format(t_total_new-t_total_old))
return pos_best_g,err_best_g,maxiter,backpropsteps
a = mnist.train_images()
X_train = mnist.train_images().reshape(a.shape[0], (a.shape[1]*a.shape[2]))
Y_train = mnist.train_labels()
a = mnist.test_images()
X_test = mnist.test_images().reshape(a.shape[0], (a.shape[1]*a.shape[2]))
Y_test = mnist.test_labels()
#oneHot = [np.zeros(3) for i in Y]
##for i in range(len(Y)):
## oneHot[i][Y[i]] = 1
#Y = np.array(oneHot)
#X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)
#n_hidden = int(sys.argv[1])
__author__ = 'Jie'
"""
a code to predict the handwriting of digits based on 3 layers nn.
the dataset is from an exsiting dataset library mnist
"""
import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import to_categorical
import numpy as np
import pandas as pd
# load data
trains_X_orig = mnist.train_images() # shape(60000,28,28)
trains_y = mnist.train_labels()
test_X_orig = mnist.test_images() #shape(10000,28,28)
test_y = mnist.test_labels()
#flatten the image data into 2D. (60000,784)
trains_X = trains_X_orig.reshape(
(trains_X_orig.shape[0], -1)) # input shape (60000,28*28)
test_X = test_X_orig.reshape((test_X_orig.shape[0], -1))
# normalize data by 255 into [-0.5,0.5]
trains_X = trains_X / 255 - 0.5
test_X = test_X / 255 - 0.5
# create model, 2 hidden layers+ 1 output layer= 3 layers nn.
model = Sequential([
Dense(64, activation='relu', input_shape=(784, )),
Dense(64, activation='relu'),
from beacon.nn.models import Sequential
from beacon.optim import SGD
from beacon.data import data_generator
from beacon.functional import functions as F
model = Sequential(
Linear(784, 64),
BatchNorm(input_shape=(1, 64)),
ReLU(),
Linear(64,10),
Softmax()
)
optimizer = SGD(model.parameters(), lr=0.01)
# Preparing training data
x_train = mnist.train_images().reshape(60000, 784) / 255.0
y_train = mnist.train_labels().reshape(60000, 1)
y_train = np.eye(10)[y_train].reshape(60000, 10)
# Preparing validation data
x_test = mnist.test_images().reshape(10000, 784) / 255.0
y_test = mnist.test_labels().reshape(10000, 1)
# Training
model.train()
for epoch in range(1, 6):
full_loss = 0
n_loss = 0
for x, y in data_generator(x_train, y_train, batch_size=128, shuffle=True):
optimizer.zero_grad()
output = model(x)
from __future__ import print_function
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
import mnist
plt.ion()
# ------------------------------------------------------------------------------
# Dati
num_clusters = 20
epoche = 30
data = mnist.train_images() / 255.0
num_patterns, pattern_side, _ = data.shape
pattern_len = pattern_side * pattern_side
data = data.reshape(num_patterns, pattern_len)
centroids = np.zeros([num_clusters, pattern_len])
# ------------------------------------------------------------------------------
# Plotting: inizializza il plot dei pesi
plot_centroids = []
fig = plt.figure(figsize=(10, 8))
for i in range(num_clusters):
ax = fig.add_subplot(4, num_clusters / 4, i + 1, aspect="equal")
model_type = 'cnn'
assert model_type in ('dnn', 'cnn')
flatten = True if model_type == 'dnn' else False
## Dataset
import mnist
from sklearn.model_selection import train_test_split
def preprocess(x, y, flatten):
x = x.astype("float32")/255.
y = y.astype("int64")
x = x.reshape(-1, 784) if flatten else x.reshape(-1, 28, 28, 1)
return x, y
images, labels = mnist.train_images(), mnist.train_labels()
x_train, x_valid, y_train, y_valid = train_test_split(images, labels,
test_size=0.2, random_state=seed)
x_test, y_test = mnist.test_images(), mnist.test_labels()
x_train, y_train = preprocess(x_train, y_train, flatten)
x_valid, y_valid = preprocess(x_valid, y_valid, flatten)
x_test, y_test = preprocess(x_test, y_test, flatten)
## Model
model = SimpleDNN() if flatten else SimpleCNN()
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy()
optim = tf.keras.optimizers.Adam()
## Training
best_loss, best_epoch, best_model = 1e12, 0, None
def Training(weightList, dimLayers):
images = mnist.train_images()
labels = mnist.train_labels()
images = images.reshape((images.shape[0], images.shape[1] * images.shape[2]))
epochs = 4
iterations = 50 # number of batches
batchSize = 1000
masks = None
dropOut = True
minP = .5
maxP = .5
# get random permutation of images and labels
combo = []
for j in range(iterations*batchSize):
combo.append((images[j],labels[j]))
random.shuffle(combo)
numLayers = len(weightList)
numNeurons = sum([pair[0]*pair[1] for pair in dimLayers])
counts = []
example = 0
for k in range(epochs):
for batch in range (iterations):
changes = [] # changes made to weights after 'batchsize' trials
error = 0
count = 0
if batch%10 == 0: print(batch)
# create masks for dropout
if dropOut:
masks = DropOut(dimLayers,minP,maxP)
for trial in range(batchSize):
# shuffle which ones drop out, faster than generating a new set of masks
if dropOut:
for mask in masks: np.random.shuffle(mask[0])
tempChanges = [] # has the changes backwards
actLayers = []
idx = batchSize * batch + trial
(img,ans) = combo[idx]
img = np.asarray([img]) / 255 # make pixel values between 0 and 1
if dropOut: img = img * masks[0]
actLayers = ProduceActLayers(img,numLayers,weightList,masks)
# Backpropagation
guess = actLayers.pop()
# ensure we don't divide by 0. Should never be triggered
if np.linalg.norm(guess) == 0: print(guess)
guess /= np.linalg.norm(guess) # normalize guess
if np.linalg.norm(guess) == 0 or np.linalg.norm(guess) > 1000:
print("norm too big")
sys.exit()
guess = np.ndarray.tolist(guess)
# if guess.index(max(guess)) == ans:
# count += 1
costs = Cost(guess, ans)
error += Error(costs)
dCdG = dCdGuess(guess, ans) # change in cost WRT final act layer
# change the learning rate
if example < 300 * batchSize: delta = 0.03
elif example < 450 * batchSize: delta = 0.015
elif example < 600 * batchSize: delta = 0.005
elif example < 800 * batchSize: delta = 0.001
else: delta = 0.0005
# dCdL is vertical, 10x1
dCdL = dCdG # dCdL is dCdG multiplied by some activation Layers in reverse
dCdL = delta * np.multiply(np.asarray([costs]).T,dCdL)
c = .06
mult=[c,78.4*c]
for layer in range(numLayers):
# dC/dW = dC/dL * dL/dW. dL/dW is the previous actLayer's neurons
if len(actLayers) != 0:
dCdW = np.multiply(dCdL, actLayers.pop()).transpose()#*mult[layer]
else:
dCdW = np.multiply(dCdL, img).transpose()#*mult[layer]
# for row in dCdW: # make each change thingy 1/(dC/dW) if it's not 0
# for i in range(len(row)):
# if row[i] != 0: row[i] = delta * row[i]
tempChanges.append(-dCdW)
# dC/dL^(i-1) = dL^i/dL^(i-1) * dC/dL^i. the second term is W^(i-1)
if (layer != numLayers - 1):
dCdL = (weightList[numLayers-1-layer] @ dCdL)
if dropOut:
dCdL *= masks[numLayers-1-layer].T
# average changes over a batch
if len(changes) == 0: # the first trial of the batch
for i in range(numLayers):
changes.append(tempChanges[numLayers - 1 - i])
else:
for i in range(numLayers):
changes[i] += tempChanges[numLayers - 1 - i]
example += 1
for layer in range(numLayers): # make changes
weightList[layer] += changes[layer]
counts.append(error)
plt.plot(counts)
plt.show()
return weightList
def test_mnist_one_hot(num_train_examples=-1, num_test_examples=-1, hidden_layers=(100,), sigmoid='tanh',
learning_rate=0.01, layer_decay=1.0, momentum=0.0, batch_size=100, num_epochs=100,
csv_filename=None, return_test_accuracies=True):
# Collect and preprocess the data.
if sigmoid == 'logistic':
train_input = convert_mnist_images_logistic(mnist.train_images()[:num_train_examples])
train_output = convert_mnist_labels_one_hot(
mnist.train_labels()[:num_train_examples], positive=0.9, negative=0.1)
test_input = convert_mnist_images_logistic(mnist.test_images()[:num_test_examples])
test_output = convert_mnist_labels_one_hot(mnist.test_labels()[:num_test_examples], positive=0.9, negative=0.1)
elif sigmoid == 'tanh':
train_input, mean_shift, std_scale = convert_mnist_images_train_tanh(mnist.train_images()[:num_train_examples])
train_output = convert_mnist_labels_one_hot(
mnist.train_labels()[:num_train_examples], positive=1.0, negative=-1.0)
test_input = convert_mnist_images_test_tanh(mnist.test_images()[:num_test_examples], mean_shift, std_scale)
test_output = convert_mnist_labels_one_hot(mnist.test_labels()[:num_test_examples], positive=1.0, negative=-1.0)
else:
raise ValueError('Invalid sigmoid function.')
# Create and train the neural network.
layer_sizes = (784,) + hidden_layers + (10,)
weight_decay = 0.0
nn = NeuralNetwork(layer_sizes, sigmoid=sigmoid, weight_decay=weight_decay)
num_examples = train_input.shape[0]
num_iterations = (num_examples // batch_size) * num_epochs
rows = None
if csv_filename is not None:
rows = []
test_accuracies = None
if return_test_accuracies:
test_accuracies = []
def callback(iteration):
if iteration % (num_examples // batch_size) == 0:
epoch = iteration // (num_examples // batch_size)
training_prediction_accuracy = get_prediction_accuracy(nn, train_input, train_output)
test_prediction_accuracy = get_prediction_accuracy(nn, test_input, test_output)
training_loss = nn.get_loss(train_input, train_output)
test_loss = nn.get_loss(test_input, test_output)
print('{},{:.6f},{:.6f},{:.6f},{:.6f}'.format(epoch, training_prediction_accuracy, test_prediction_accuracy,
training_loss, test_loss))
if csv_filename is not None:
rows.append((epoch, training_prediction_accuracy, test_prediction_accuracy, training_loss, test_loss))
if return_test_accuracies:
test_accuracies.append(test_prediction_accuracy)
print('Network Parameters')
print('layer_sizes: {}, sigmoid: {}, weight_decay: {}'.format(layer_sizes, sigmoid, weight_decay))
print('Training Parameters')
print('num_iterations: {}, learning_rate: {}, layer_decay: {}, momentum: {}, batch_size: {}'.format(
num_iterations, learning_rate, layer_decay, momentum, batch_size))
print('')
header = 'epoch,training_accuracy,test_accuracy,training_loss,test_loss'
print(header)
stochastic_gradient_descent(nn, train_input, train_output, num_iterations=num_iterations,
learning_rate=learning_rate, layer_decay=layer_decay,
momentum=momentum, batch_size=batch_size,
callback=callback)
if csv_filename is not None:
save_rows_to_csv(csv_filename, rows, header.split(','))
if return_test_accuracies:
return test_accuracies
import numpy as np
import mnist
from model.network import Net
print('Loadind data......')
num_classes = 10
train_images = mnist.train_images() #[60000, 28, 28]
train_labels = mnist.train_labels()
test_images = mnist.test_images()
test_labels = mnist.test_labels()
print('Preparing data......')
train_images = (train_images - np.mean(train_images))/np.std(train_images)
test_images = (test_images - np.mean(test_images))/np.std(test_images)
#train_images = train_images/255
#test_images = test_images/255
training_data = train_images.reshape(60000, 1, 28, 28)
training_labels = np.eye(num_classes)[train_labels]
testing_data = test_images.reshape(10000, 1, 28, 28)
testing_labels = np.eye(num_classes)[test_labels]
net = Net()
#print('Training Lenet......')
#net.train(training_data, training_labels, 100, 1, 'weights_fp.pkl')
#print('Testing Lenet......')
#net.test(testing_data, testing_labels, 100)
print('Testing with pretrained weights......')
net.test_with_pretrained_weights(testing_data, testing_labels, 1, 'pretrained_weights.pkl')
print('Predicting with pretrained weights......')
print(net.predict_with_pretrained_weights(testing_data[0], 'pretrained_weights.pkl'))
import numpy as np
import matplotlib as matplotlib
import mnist
X_train = mnist.train_images()
Y_train = mnist.train_labels()
X_test = mnist.test_images()
Y_test = mnist.test_labels()
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1]**2)) / 255
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1]**2)) / 255
print(X_train.shape)
y = np.eye(10)[Y_train.astype('int32')]
Y_train = y
y = np.eye(10)[Y_test.astype('int32')]
Y_test = y
def relu(z):
z[z < 0] = 0
return z
def relu1(z):
z[z <= 0] = 0
z[z > 0] = 1
return z
idcs = np.argsort(abs(d))
# consequently sort the eigenvectors
E = E[:, idcs[::-1]]
# chose the first 'num_PCs' components
E = E[:, :num_PCs]
# project the original data on the chosen components
pca_data = np.dot(X, E)
return pca_data, E, d[idcs[::-1]][:num_PCs]
if __name__ == "__main__":
import matplotlib.pyplot as plt
import mnist
data = mnist.train_images()[:200] / 255.0
labels = mnist.train_labels()[:200]
num_patterns, pattern_side, _ = data.shape
pattern_len = pattern_side * pattern_side
data = data.reshape(num_patterns, pattern_len)
data_out, E, d = PCA(data, 2)
plt.imshow(E[:, 1].reshape(pattern_side, pattern_side))
plt.show()
plt.hist(labels)
plt.show()
from mnist import train_images, test_images, train_labels, test_labels
if __name__ == '__main__':
X_train, Y_train = train_images(), train_labels()
X_test, X_test = test_images(), test_labels()
import numpy as np
import mnist
import matplotlib.pyplot as plt
from sklearn import linear_model
from sklearn.metrics import accuracy_score
N = 1000
train_imgs = mnist.train_images()[:N]
train_labels = mnist.train_labels()[:N]
Xtrain_all = np.asarray(train_imgs)
ytrain_all = np.array(train_labels.tolist())
dimension = Xtrain_all.shape[1] * Xtrain_all.shape[2]
print(dimension)
test_imgs = mnist.test_images()[:N]
test_labels = mnist.test_labels()[:N]
Xtest_all = np.asarray(test_imgs)
ytest_all = np.array(test_labels.tolist())
cls = [[0], [1]]
img_size = Xtrain_all.shape[1] * Xtrain_all.shape[2]
def extract_data(X, y, classes):
"""
X: numpy array, matrix of size (N, d), d is data dim
y: numpy array, size (N, )
cls: two lists of labels. For example:
cls = [[1, 4, 7], [5, 6, 8]]
return:
X: extracted data
import numpy as np
import mnist
import tensorflow as tf
from tensorflow import keras
# Load Mnist Data
print('Loading Data...')
X_train, y_train, X_test, y_test = (mnist.train_images(), mnist.train_labels(),
mnist.test_images(), mnist.test_labels())
# Normalize Data
X_train = (X_train / 255)
X_test = (X_test / 255)
# Augment images to account for cell boundaries
def augment_img(image):
img = image.copy()
top, right, bot, left = np.random.random(4) > 0.6
if top:
size = np.random.randint(0, 3)
img[:size] = min(np.abs(np.random.normal(loc=0.5, scale=0.5)), 1)
if right:
size = np.random.randint(0, 3)
img[:, 28 - size:] = min(np.abs(np.random.normal(loc=0.5, scale=0.5)),
1)
if bot:
size = np.random.randint(0, 3)
img[28 - size:] = min(np.abs(np.random.normal(loc=0.5, scale=0.5)), 1)
if left:
def get_train_data():
train_images = mnist.train_images()
return train_images.reshape(reshape_image_to_1D(train_images))
# Description: This program classifies the MNIST handwritten digit images # as a number 0 - 9 # Install packages pip install tensorflow keras numpy mnist matplotlib #import the packages / dependecies import numpy as np import mnist # Get data set from from keras.models import Sequential #ANN architecture from keras.layers import Dense # The layers in the ANN from keras.utils import to_categorical import matplotlib.pyplot as plt # Graph #Load the data set train_images = mnist.train_images() # training data of images train_labels = mnist.train_labels() # training data of the labels test_images = mnist. test_images() # testing data images test_labels = mnist.test_labels() # testing data labels #Normalize the images #Normalize the pixel values from [0, 255] to [-0.5 to 0.5] #This make the network easier to train train_images = (train_images / 255) - 0.5 test_images = (test_images/ 255) - 0.5 #Flatten the images. Flatten each 28 x 28 image into a 784= 28^2 #dimensional vector and pass into the neural network train_images = train_images.reshape((-1, 784)) test_images = test_images.reshape((-1,784)) #print the new image shape
wigstring += '\n\n\n'
wigstring += 'who\n'
wigstring += str(who)
wigstring += '\n\n\n'
wigstring += 'bho\n'
wigstring += str(bho)
wigstring += '\n\n\n'
file.write(wigstring)
file.close()
train_data = mnist.train_images()
train_label = mnist.train_labels()
testing_data = mnist.test_images()
testing_label = mnist.test_labels()
train_data=1./train_data.max()*(train_data-train_data.mean())
testing_data=1./testing_data.max()*(testing_data-testing_data.mean())
start =time.time() # get current time
train_accu=[]
epoch_time=1
batch_size=250
for i in range(epoch_time):
arr=np.arange(50000)
# this program classifies the MNIST handwritten digit images import numpy as np import mnist # The Data set import matplotlib.pyplot as plt from keras.models import Sequential # Ann architecture from keras.layers import Dense # Will provide the layers for ann from keras.utils import to_categorical # Loading the data set train_images = mnist.train_images() # Training data images train_labels = mnist.train_labels() # Training data labels test_images = mnist.test_images() # Training data images test_labels = mnist.test_labels() # Training data labels # Normalizing the images. Normalizing from [0, 255] to [-0.5, 05] # This is to make the ann easier to train train_images = (train_images / 255) - 0.5 test_images = (test_images / 255) - 0.5 # Flatten the images. I am going to flatten each 28 * 28 image into a vector # 28^2 = 784, This is the dimensional vector # To pass into the ann train_images = train_images.reshape((-1, 784)) test_images = test_images.reshape((-1, 784)) # Print the shape of the images print(train_images.shape) # (60000(rows), 784(columns) )
# Classify MNIST handwritten digit images as 0 - 9
# Import dependencies
import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import to_categorical
import mnist
# Load the data
train_images = mnist.train_images(
) # training data images - stored in numpy arrays
print("The initial size of the training images: ", train_images.shape) # 60k
train_labels = mnist.train_labels() # training data labels
print("The number of training labels is: ", train_labels.shape) # 60k
test_images = mnist.test_images() # testing data images
print("The initial size of the testing images: ", test_images.shape)
test_labels = mnist.test_labels() # testing data labels
print("The initial size of the testing labels: ", test_labels.shape)
# Normalize the images - normalize pixel values from [0, 255] to [-0.5, 0.5] to make our network easier to train
train_images = (train_images / 255) - 0.5
test_images = (test_images / 255) - 0.5
# Flatten the images - flatten each 28 x 28 image into a 784 dimensional vector to pass to NN
train_images = train_images.reshape((-1, 784))
test_images = test_images.reshape((-1, 784))
# Print the shape
print(train_images.shape) # 60,000 rows and 784 columns
bv4 = beta1*bv4 + (1-beta1)*db4/batch_size
bs4 = beta2*bs4 + (1-beta2)*(db4/batch_size)**2
b4 -= lr * bv4 / np.sqrt(bs4+1e-7)
params = [f1, f2, w3, w4, b1, b2, b3, b4]
return params
# In[10]:
num_images = 60000
batch_size = 32
X = np.asarray(mnist.train_images()[:num_images],dtype=np.float32)
y = mnist.train_labels()[:num_images]
num_classes = 10
imgdim = X.shape[1]
# Random Shuffle the data
permutation = np.random.permutation(num_images)
X = X[permutation]
y = y[permutation]
# Normalizing the images
X = X - int(np.mean(X))
X = X / int(np.std(X))
Y = np.array([np.eye(num_classes)[int(y[k])].reshape(num_classes, 1) for k in range(0,num_images)])
X = X.reshape(num_images, 1, imgdim, imgdim)
#Import Packages import numpy as np import mnist #Get Data Set From import matplotlib.pyplot as plt #Graph from keras.models import Sequential #ANN Architecture from keras.layers import Dense #The Layers In The ANN from keras.utils import to_categorical #Load Data To Set train_images = mnist.train_images() #Training Data - Images train_labels = mnist.train_labels() #Training Data - Labels test_images = mnist.test_images() #Test Data - Images test_labels = mnist.test_labels() #Test Data - Labels #Normalize The Images. Normalize The Pixel Values From [0, 255] #To [-0.5, 0.5] To Make Our Network Easier To Train train_images = (train_images / 255) - 0.5 test_images = (test_images / 255) - 0.5 #Flatten The Images - Each 28X28 Into A 784 Dimensional Vector To Pass Into NN train_images = train_images.reshape((-1, 784)) test_images = test_images.reshape((-1, 784)) #Print The Shape print(train_images.shape) #60,000 Rows, 784 Rows print(test_images.shape) #10,000 Rows, 784 Rows #Build The Model #With 3 Layers, #2 Layers With 64 Neurons And The Relu Function, #1 Layer With 10 Neurons And Softmax Function model = Sequential() model.add(Dense(64, activation='relu', input_dim=784))
import mnist
import numpy as np
import matplotlib.pyplot as plt
images = np.append(mnist.train_images(), mnist.test_images(), axis=0)
labels = np.append(mnist.train_labels(), mnist.test_labels(), axis=0)
np.save('mnist_images.npy', images)
np.save('mnist_labels.npy', labels)
class ProtoDash():
def create_spark_session():
global sc
sc = SparkContext.getOrCreate()
sc.setLogLevel("ERROR")
return SparkSession(sc)
def create_vec_rdd(X, part=4):
"""
Function returning a DenseVector RDD from a dataset X.
Args:
-X: a dataset with rows corresponding to observations and columns corresponding to features.
-part: n of partitions.
Returns: the RDD for X
"""
# creating a Spark session
spark_session = ProtoDash.create_spark_session()
X_rdd = (sc.parallelize(
X, part).map(lambda x: DenseVector(x)).zipWithIndex())
return X_rdd
def mean_inner_product(inp, sigma, n):
"""
Function computing the gaussian kernel inner product of a vector in Y vs.
a vector in X, divided by n the total number of observations in X.
"""
index_1 = inp[0][1]
inner_product = float(
np.exp(inp[0][0].squared_distance(inp[1][0]) / (-2 * sigma**2)) /
n)
return (index_1, inner_product)
def inner_product(inp, sigma):
"""
Function computing the gaussian kernel inner product of a vector vs.
another.
"""
index_1 = inp[0][1]
index_2 = inp[1][1]
inner_product = float(
np.exp(inp[0][0].squared_distance(inp[1][0]) / (-2 * sigma**2)))
return (index_1, [(index_2, inner_product)])
def weighted_sum(inp, w_arr):
"""
compute the weighted sum of matrix values for a set of indices and weights.
Note it is fine using a list comprehension here since the number of prototypes m << |X^{(1)}|.
"""
return float(np.sum(np.array([x[1] for x in inp]) * w_arr))
def udf_weighted_sum(w_arr):
"""
UDF instance of the weighted_sum function.
"""
return F.udf(lambda l: ProtoDash.weighted_sum(l, w_arr))
def merge_lists(x, y):
"""
merge lists.
"""
return sorted(x + y, key=lambda tup: tup[0])
# Create UDF corresponding to merge_lists function.
DType = ArrayType(
StructType(
[StructField("_1", LongType()),
StructField("_2", FloatType())]))
udf_merge_lists = F.udf(merge_lists, DType)
def optimize(K, u, opt_w0, init_val, max_w=10000):
"""
Function solving quadratic optimization problem.
Args:
-K: inner product matrix
-u: mean inner product of each prototype
-opt_w0: initial weights vector
-init_val: starting run
-max_w: an upper bound on weight value
Returns:
-weights and the objective value
"""
dim = u.shape[0]
low_b = np.zeros((dim, 1))
upper_b = max_w * np.ones((dim, 1))
x_init = np.append(opt_w0, init_val / K[dim - 1, dim - 1])
G = np.vstack((np.identity(dim), -1 * np.identity(dim)))
h = np.vstack((upper_b, -1 * low_b))
# solve constrained quadratic problem
soltn = solve_qp(K,
-u,
G,
h,
A=None,
b=None,
solver='cvxopt',
initvals=x_init)
# calculate the objective function value for optimal solution
x_sol = soltn.reshape(soltn.shape[0], 1)
q = -u.reshape(u.shape[0], 1)
obj_value = 1 / 2 * np.matmul(np.matmul(x_sol.T, K),
x_sol) + np.matmul(q.T, x_sol)
return (soltn, obj_value[0, 0])
def ProtoDashAlgoritm(X, Y, m, sigma, partitions=20, verbose=True):
"""
Implementation of the ProtoDash algorithm
Args:
-X (RDD of indexed DenseVector rows): Target dataset/ the dataset to be represented.
-Y (RDD of indexed DenseVector rows): Source dataset/ the dataset to select prototypes from.
-m (integer): total number of prototypes to select.
-sigma (strictly positive float): gaussian kernel parameter.
-partitions (integer): number of RDD partitions to compute inner product RDDs with.
-verbose (boolean): whether or not to print the cumulative number of prototypes selected at each iteration.
Returns:
-L (integer list): the set of indices corresponding to selected prototypes.
-w (float list): the optimal set of weights corresponding to each selected prototype.
"""
# get count of observations in X
n_X = X.count()
# build mu DataFrame
mu_df = (Y.cartesian(X).map(lambda x: ProtoDash.mean_inner_product(
x, sigma, n_X)).reduceByKey(lambda x, y: x + y).toDF(["obs",
"mu"]))
# initialise key variables
L = np.zeros(m, dtype=int) # set of prototype indices L
w = np.zeros(m, dtype=float) # set of optimal prototype weights
f_eval = np.zeros(
m, dtype=float) # set of the f(w) eval. at prototype selection
n_L = 0 # count of prototypes selected so far
# find the index corresponding to the maximum mu value
max_grad_0 = mu_df.orderBy(F.desc("mu")).limit(1).collect()[0]
# collect values
L[n_L] = max_grad_0.obs
w[n_L] = max_grad_0.mu
f_eval[n_L] = 1 / 2 * max_grad_0.mu**2
n_L += 1
# select the row of Y corresponding to the first chosen index
Y_row_j0 = Y.filter(lambda x: x[1] == L[:n_L]).collect()[0]
# take its inner product with all rows of Y to build the starting K dataframe
K_init_df = (Y.map(lambda x: ProtoDash.inner_product(
(x, Y_row_j0), sigma)).toDF(["obs", "K"]))
# join mu and K dataframes
join_df = (mu_df.join(K_init_df, "obs").repartition(partitions))
# cache join_df as it is reused often
join_df.cache()
# compute the new gradient vector
grad_df = (join_df.withColumn(
"K_weighted",
ProtoDash.udf_weighted_sum(w[:n_L])(F.col("K"))).withColumn(
"grad",
F.col("mu") - F.col("K_weighted")).select("obs", "grad"))
# begin while loop
while n_L < m:
# remove the rows that have an index already included in L
grad_df = grad_df.filter(
~grad_df.obs.isin([int(x) for x in L[:n_L]]))
# find the row that has the maximum value in the filtered gradient vector
argmax_grad = grad_df.orderBy(F.desc("grad")).limit(1).collect()[0]
# update L
L[n_L] = argmax_grad.obs
# select the row of Y corresponding to the chosen index
Y_row_j = Y.filter(lambda x: x[1] == L[n_L]).collect()[0]
# take its inner product with all rows of Y to build new K
K_int_df = (Y.map(lambda x: ProtoDash.inner_product(
(x, Y_row_j), sigma)).toDF(["obs", "new_K_col"]))
# add new K col to previous K col
join_df = (join_df.join(K_int_df, "obs").withColumn(
"K_merged",
ProtoDash.udf_merge_lists(F.col("K"),
F.col("new_K_col"))).select(
"obs", "mu",
"K_merged").withColumnRenamed(
"K_merged", "K"))
# cache new joined_df
join_df.cache()
# increment n_L
n_L += 1
# sort L
L[:n_L] = sorted(L[:n_L])
if verbose is True and n_L % 5 == 0:
print("Prototypes selected - " + str(n_L))
# take max gradient val.
max_grad = argmax_grad.grad
# filter join dataframe for given indices in L
filt_df = (join_df.filter(
join_df.obs.isin([int(x) for x in L[:n_L]
])).orderBy(F.col("obs").asc()))
# take mu vector
mu_arr = np.array(filt_df.select("mu").collect(), dtype=float)
# take K matrix
K_mat = np.array(
filt_df.rdd.map(lambda x: [y[1] for y in x[2]]).collect(),
dtype=float)
# find optimal weights for the index set L
opt_res = ProtoDash.optimize(K_mat, mu_arr, w[:n_L - 1], max_grad)
(w[:n_L], f_eval[n_L - 1]) = opt_res[0], -opt_res[1]
# compute gradient vector with new optimal weights
grad_df = (join_df.withColumn(
"K_weighted",
ProtoDash.udf_weighted_sum(w[:n_L])(F.col("K"))).withColumn(
"grad",
F.col("mu") - F.col("K_weighted")).select("obs", "grad"))
# tuple of indices and their corresponding weight, sorted by weight in descending order.
res = sorted([(w[i], L[i]) for i in range(m)], key=lambda tup: -tup[0])
# return tuple of index set L and optimal weight set w, set of f_eval
return res, f_eval
#######################################################
######### RDF IMPLEMENTATION ###########
#######################################################
rdf_dataset = None
numeric_dataset = None
subject_index = {}
predicate_index = {}
object_index = {}
def infer_index(token, token_indices):
"""
Enumerate the distinct tokens. If the token is found in the token_indices, then return it,
else assign the next integer number (after the last assigned index) which is also the size of the token_indices.
"""
if token in token_indices:
return token_indices[token]
else:
token_index = len(token_indices)
token_indices[token] = token_index
return token_index
def convert_rdf_to_ntriples(dataset):
"""
Loads rdf data and converts into n-triples, treating each triple as a datapoint in the dataset.
"""
g = Graph()
g.load(dataset)
rem_object = URIRef(
"http://www.w3.org/2002/07/owl#NamedIndividual"
) # deleting the triples that have object value as 'owl#NamedIndividual'
for s, p, o in g:
g.remove((s, p, rem_object))
global rdf_dataset
global numeric_dataset
# create n-triples of strings
rdf_dataset = [(str(s), str(p), str(o)) for s, p, o in g]
# preprocess and create a numeric dataset in order to input to ProtoDash
numeric_dataset = list(
map(
lambda e:
(ProtoDash.infer_index(e[0], ProtoDash.subject_index),
ProtoDash.infer_index(e[1], ProtoDash.predicate_index),
ProtoDash.infer_index(e[2], ProtoDash.object_index)),
rdf_dataset))
#print(rdf_dataset)
#print('************************************')
#print('Size of dataset:', len(rdf_dataset))
#print('Subjects cardinality:', len(subject_index))
#print('Predicates cardinality:', len(predicate_index))
#print('Objects cardinality:', len(object_index))
print('************************************')
return numeric_dataset
def strip_rdf_prefix(triple):
"""
Strips the common URL-like prefixes from the RDF data and takes the suffix after '#'.
Example:
Input triple: ('http://www.semanticweb.org/vinu/ontologies/2014/6/untitled-ontology-91#naomi_watts',
'http://www.semanticweb.org/vinu/ontologies/2014/6/untitled-ontology-91#acted_in',
'http://www.semanticweb.org/vinu/ontologies/2014/6/untitled-ontology-91#rabbits')
Output: naomi_watts acted_in rabbits
"""
return ' '.join(tuple(map(lambda e: e[e.find('#') + 1:], triple)))
def get_sample_index(dataset, sample):
# Function returns the index of the triple in the dataset
global dataset_rdd
dataset_rdd = ProtoDash.convert_rdf_to_ntriples(dataset)
index_list = [x for x, y in enumerate(rdf_dataset)]
for i in range(len(rdf_dataset)):
if rdf_dataset[i] == sample:
return index_list[i]
def get_rdf_prototypes(dataset, sample_triple, num_proto):
# Index of the sample from the dataset to be given to the ProtoDash as dataset to be explained
# These prototypes that come out of ProtoDash can be thought as the cluster that this sample belongs to.
# Or vice versa, the sampled datapoint can be thought as cluster centroid, and the explaining prototypes
# as the data that belong to that cluster.
sample_index = ProtoDash.get_sample_index(dataset, sample_triple)
if sample_index is not None:
# Create a target dataset comprising of the selected sample
target = [numeric_dataset[sample_index]]
# Create a source dataset comprising of all triples but the selected sample
source = numeric_dataset[:sample_index] + numeric_dataset[
sample_index + 1:]
# Convert the datasets to PySpark RDDs
target_rdd = ProtoDash.create_vec_rdd(target)
source_rdd = ProtoDash.create_vec_rdd(source)
print('Starting ProtoDash on RDF')
res, f = ProtoDash.ProtoDashAlgoritm(target_rdd,
source_rdd,
num_proto,
50,
partitions=4,
verbose=True)[:2]
print('Finished ProtoDash on RDF')
print('The chosen sample_index:', sample_index)
# Raw RDF triples has a long common prefixes, for the sake presentation (to keep it short),
# I strip the common long URL-like prefixes and take the suffix after '#' - the data that matters.
stripped_target = ProtoDash.strip_rdf_prefix(
rdf_dataset[sample_index])
# Print the target datapoint
print('Target (sampled) datapoint: ', stripped_target)
# create the Y and X axis of the plot
# The result (res) that comes from the ProtoDash is a list of pairs of weight and index
# I use the index find the triples from the raw dataset to be used X-axis
# and the weights are used as Y-coordinates
values = list(map(lambda e: e[0], res)) # e[0] is weight
names = list(map(lambda e: rdf_dataset[e[1]],
res)) # e[1] is index
# strip the names to fit into the plot
names = list(map(ProtoDash.strip_rdf_prefix, names))
plt.barh(names, values)
plt.title(stripped_target)
plt.show()
else:
print("Please enter a valid triple")
def ProtoDashOnRDF(dataset, num_proto, sample_triple):
# dataset: path to the file
# num_proto: number of prototypes for ProtoDash to select
# sample_triple: the sample_triple is string which refer to the triple
if os.path.isfile(dataset):
if num_proto.isdigit():
sample_triple = tuple(sample_triple.split(','))
ProtoDash.get_rdf_prototypes(dataset, sample_triple,
int(num_proto))
else:
print("Number of prototypes can be only integer")
else:
print("File do not exists")
#######################################################
######### Image IMPLEMENTATION ###########
#######################################################
# collect MNIST train/test sets
train_images = np.array(mnist.train_images(), dtype='float')
train_labels = mnist.train_labels()
test_images = np.array(mnist.test_images(), dtype='float')
test_labels = mnist.test_labels()
def create_target_set(labels, images, digit, target_n, percentage):
"""
This function creates a MNIST image dataset in which a specified percentage of the total observations
correspond to a specific digit, while the remaining observations correspond to other randomly
chosen digits.
Args:
-labels: the digit label for each MNIST image.
-images: the MNIST image.
-digit: a digit between 0 and 9.
-target_n: the number of total observations required in the target dataset.
-percentage: the percentage of images in the target dataset that correspond to the specified digit.
Returns:
-the target images.
"""
# take integer number of obs. corresponding to digit
n_dig = int(np.floor(percentage * target_n))
# get indices corresponding to digit
idx = np.where(labels == digit)[0]
# reduce indices to specific %
idx_red = idx[:n_dig]
# slice images with index and reshape
target_set_dig = images[idx_red, :]
target_set_dig = np.reshape(target_set_dig,
(target_set_dig.shape[0], 28 * 28))
# get remaining indices
rem = target_n - n_dig
rem_ind = np.setdiff1d(np.arange(len(labels)), idx_red)[:rem]
# fill the remaining observations with images corresponding to other digits
target_set_non_dig = images[rem_ind]
target_set_non_dig = np.reshape(target_set_non_dig,
(target_set_non_dig.shape[0], 28 * 28))
# create the dataset
target_set = np.vstack((target_set_non_dig, target_set_dig))
# shuffle it
arr = np.arange(target_n)
np.random.shuffle(arr)
return target_set
def get_image_prototypes(num_proto, digit):
part = 6 # number of Pyspark RDD partitions to use
sigma = 50 # gaussian kernel parameter
n_1 = 5420 # the number of observations in X_1
n_2 = 1500 # the number of observations in X_2
#percentages = [.3, .5, .7, .9, 1.]
percentages = [
1.
] # the percentage of X_1 that will correspond to the chosen digit
# list of experiment results
exp_1_res_list = []
# list of f_eval sequences
exp_1_f_eval_list = []
# set source dataset and labels
source_set = np.reshape(ProtoDash.test_images[:n_2], (n_2, 28 * 28))
# select the target datasets
target_set = ProtoDash.create_target_set(ProtoDash.train_labels,
ProtoDash.train_images, digit,
n_1, 1)
# convert target and source datasets to RDDs
target_rdd = ProtoDash.create_vec_rdd(target_set, part)
source_rdd = ProtoDash.create_vec_rdd(source_set, part)
# collect the indices of m prototypes along with their ascribed weight
res, f = ProtoDash.ProtoDashAlgoritm(target_rdd,
source_rdd,
num_proto,
sigma,
partitions=part,
verbose=True)[:2]
# collect the results
exp_1_res_list.append(res)
exp_1_f_eval_list.append(f)
fig, axes = plt.subplots(num_proto, 1, figsize=(12, 10), squeeze=False)
for i in range(num_proto):
for j in range(len(percentages)):
axes[i][j].imshow(
np.reshape(source_set[exp_1_res_list[j][i][1], :],
(28, 28)))
axes[i][j].get_xaxis().set_ticks([])
axes[i][j].get_yaxis().set_ticks([])
fig.suptitle("\n".join(
wrap(
"Top %d prototypes selected by ProtoDash corresponding to the digit %d"
% (num_proto, digit), 60)),
fontsize=20)
plt.show()
spark.stop()
def ProtoDashOnImage(digit, num_proto):
# digit: the digit to be represented in the target dataset X_1
# num_proto: number of prototypes for ProtoDash to select
if digit.isdigit() and 0 <= int(digit) <= 9:
if num_proto.isdigit():
ProtoDash.get_image_prototypes(int(num_proto), int(digit))
else:
print("Please enter an integer value for number of prototypes")
else:
print("Please enter a digit between 0-9")
def main():
# XOR revisited
# training data
xs = [[0., 0], [0., 1], [1., 0], [1., 1]]
ys = [[0.], [1.], [1.], [0.]]
random.seed(0)
net = Sequential([
Linear(input_dim=2, output_dim=2),
Sigmoid(),
Linear(input_dim=2, output_dim=1)
])
import tqdm
optimizer = GradientDescent(learning_rate=0.1)
loss = SSE()
with tqdm.trange(3000) as t:
for epoch in t:
epoch_loss = 0.0
for x, y in zip(xs, ys):
predicted = net.forward(x)
epoch_loss += loss.loss(predicted, y)
gradient = loss.gradient(predicted, y)
net.backward(gradient)
optimizer.step(net)
t.set_description(f"xor loss {epoch_loss:.3f}")
for param in net.params():
print(param)
# FizzBuzz Revisited
from scratch.neural_networks import binary_encode, fizz_buzz_encode, argmax
xs = [binary_encode(n) for n in range(101, 1024)]
ys = [fizz_buzz_encode(n) for n in range(101, 1024)]
NUM_HIDDEN = 25
random.seed(0)
net = Sequential([
Linear(input_dim=10, output_dim=NUM_HIDDEN, init='uniform'),
Tanh(),
Linear(input_dim=NUM_HIDDEN, output_dim=4, init='uniform'),
Sigmoid()
])
def fizzbuzz_accuracy(low: int, hi: int, net: Layer) -> float:
num_correct = 0
for n in range(low, hi):
x = binary_encode(n)
predicted = argmax(net.forward(x))
actual = argmax(fizz_buzz_encode(n))
if predicted == actual:
num_correct += 1
return num_correct / (hi - low)
optimizer = Momentum(learning_rate=0.1, momentum=0.9)
loss = SSE()
with tqdm.trange(1000) as t:
for epoch in t:
epoch_loss = 0.0
for x, y in zip(xs, ys):
predicted = net.forward(x)
epoch_loss += loss.loss(predicted, y)
gradient = loss.gradient(predicted, y)
net.backward(gradient)
optimizer.step(net)
accuracy = fizzbuzz_accuracy(101, 1024, net)
t.set_description(f"fb loss: {epoch_loss:.2f} acc: {accuracy:.2f}")
# Now check results on the test set
print("test results", fizzbuzz_accuracy(1, 101, net))
random.seed(0)
net = Sequential([
Linear(input_dim=10, output_dim=NUM_HIDDEN, init='uniform'),
Tanh(),
Linear(input_dim=NUM_HIDDEN, output_dim=4, init='uniform')
# No final sigmoid layer now
])
optimizer = Momentum(learning_rate=0.1, momentum=0.9)
loss = SoftmaxCrossEntropy()
with tqdm.trange(100) as t:
for epoch in t:
epoch_loss = 0.0
for x, y in zip(xs, ys):
predicted = net.forward(x)
epoch_loss += loss.loss(predicted, y)
gradient = loss.gradient(predicted, y)
net.backward(gradient)
optimizer.step(net)
accuracy = fizzbuzz_accuracy(101, 1024, net)
t.set_description(f"fb loss: {epoch_loss:.3f} acc: {accuracy:.2f}")
# Again check results on the test set
print("test results", fizzbuzz_accuracy(1, 101, net))
# Load the MNIST data
import mnist
# This will download the data, change this to where you want it.
# (Yes, it's a 0-argument function, that's what the library expects.)
# (Yes, I'm assigning a lambda to a variable, like I said never to do.)
mnist.temporary_dir = lambda: '/tmp'
# Each of these functions first downloads the data and returns a numpy array.
# We call .tolist() because our "tensors" are just lists.
train_images = mnist.train_images().tolist()
train_labels = mnist.train_labels().tolist()
assert shape(train_images) == [60000, 28, 28]
assert shape(train_labels) == [60000]
import matplotlib.pyplot as plt
fig, ax = plt.subplots(10, 10)
for i in range(10):
for j in range(10):
# Plot each image in black and white and hide the axes.
ax[i][j].imshow(train_images[10 * i + j], cmap='Greys')
ax[i][j].xaxis.set_visible(False)
ax[i][j].yaxis.set_visible(False)
# plt.show()
# Load the MNIST test data
test_images = mnist.test_images().tolist()
test_labels = mnist.test_labels().tolist()
assert shape(test_images) == [10000, 28, 28]
assert shape(test_labels) == [10000]
# Recenter the images
# Compute the average pixel value
avg = tensor_sum(train_images) / 60000 / 28 / 28
# Recenter, rescale, and flatten
train_images = [[(pixel - avg) / 256 for row in image for pixel in row]
for image in train_images]
test_images = [[(pixel - avg) / 256 for row in image for pixel in row]
for image in test_images]
assert shape(train_images) == [60000, 784], "images should be flattened"
assert shape(test_images) == [10000, 784], "images should be flattened"
# After centering, average pixel should be very close to 0
assert -0.0001 < tensor_sum(train_images) < 0.0001
# One-hot encode the test data
train_labels = [one_hot_encode(label) for label in train_labels]
test_labels = [one_hot_encode(label) for label in test_labels]
assert shape(train_labels) == [60000, 10]
assert shape(test_labels) == [10000, 10]
# Training loop
import tqdm
def loop(model: Layer,
images: List[Tensor],
labels: List[Tensor],
loss: Loss,
optimizer: Optimizer = None) -> None:
correct = 0 # Track number of correct predictions.
total_loss = 0.0 # Track total loss.
with tqdm.trange(len(images)) as t:
for i in t:
predicted = model.forward(images[i]) # Predict.
if argmax(predicted) == argmax(labels[i]): # Check for
correct += 1 # correctness.
total_loss += loss.loss(predicted, labels[i]) # Compute loss.
# If we're training, backpropagate gradient and update weights.
if optimizer is not None:
gradient = loss.gradient(predicted, labels[i])
model.backward(gradient)
optimizer.step(model)
# And update our metrics in the progress bar.
avg_loss = total_loss / (i + 1)
acc = correct / (i + 1)
t.set_description(f"mnist loss: {avg_loss:.3f} acc: {acc:.3f}")
# The logistic regression model for MNIST
random.seed(0)
# Logistic regression is just a linear layer followed by softmax
model = Linear(784, 10)
loss = SoftmaxCrossEntropy()
# This optimizer seems to work
optimizer = Momentum(learning_rate=0.01, momentum=0.99)
# Train on the training data
loop(model, train_images, train_labels, loss, optimizer)
# Test on the test data (no optimizer means just evaluate)
loop(model, test_images, test_labels, loss)
# A deep neural network for MNIST
random.seed(0)
# Name them so we can turn train on and off
dropout1 = Dropout(0.1)
dropout2 = Dropout(0.1)
model = Sequential([
Linear(784, 30), # Hidden layer 1: size 30
dropout1,
Tanh(),
Linear(30, 10), # Hidden layer 2: size 10
dropout2,
Tanh(),
Linear(10, 10) # Output layer: size 10
])
# Training the deep model for MNIST
optimizer = Momentum(learning_rate=0.01, momentum=0.99)
loss = SoftmaxCrossEntropy()
# Enable dropout and train (takes > 20 minutes on my laptop!)
dropout1.train = dropout2.train = True
loop(model, train_images, train_labels, loss, optimizer)
# Disable dropout and evaluate
dropout1.train = dropout2.train = False
loop(model, test_images, test_labels, loss)
vectorized_y = np.zeros(shape)
for idx, label in enumerate(ys):
vectorized_y[idx][label] = 1
data = []
for x, y in zip(xs, vectorized_y):
data.append((x.reshape(-1, 1).astype(float), y.reshape(-1, 1)))
else:
data = []
for x, y in zip(xs, ys):
data.append((x.reshape(-1, 1).astype(float), y.reshape(-1, 1)))
return data
train = mnist.train_images()[:-10000], mnist.train_labels()[:-10000]
validation = mnist.train_images()[-10000:], mnist.train_labels()[-10000:]
test = mnist.test_images(), mnist.test_labels()
train = format_data(*train)
validation = format_data(*validation, vector_y=False)
test = format_data(*test, vector_y=False)
# Build the network
net = Network([784, 50, 50, 10])
# Train the network
net.SGD(train, 30, 10, 3, validation)
# Test the network
print("Test data: {:3.2f}% correct".format(100 * net.evaluate(test) /
