def load_MNIST_dataset():
mnist = tf.keras.datasets.mnist
(Xs_tr, Ys_tr), (Xs_te, Ys_te) = mnist.load_data()
Xs_tr = Xs_tr / 255.0
Xs_te = Xs_te / 255.0
Xs_tr = Xs_tr.reshape(Xs_tr.shape[0], 28, 28,
1) # 28 rows, 28 columns, 1 channel
Xs_te = Xs_te.reshape(Xs_te.shape[0], 28, 28, 1)
return (Xs_tr, Ys_tr, Xs_te, Ys_te)
def main():
x_train, y_train, x_test, y_test = mnist.load_data(reshape=[784, 1])
# (50000, 784) (50000, 10) (10000, 784) (10000, 10)
print('x_train, y_train, x_test, y_test:', x_train.shape, y_train.shape,
x_test.shape, y_test.shape)
np.random.seed(66)
model = Network([784, 30, 10])
data_train = list(zip(x_train, y_train))
data_test = list(zip(x_test, y_test))
model.SGD(data_train, 10000, 10, 0.1, data_test)
def main():
type = sys.argv[1]
li = convert_to_list(sys.argv[2])
li[-1] = 10
epoch = int(sys.argv[3])
lrate = float(sys.argv[4])
if (type == '1'):
img_name = sys.argv[5]
img = imread(img_name)
img = np.reshape(img, (28 * 28, 1))
print 'loading...'
li[0] = img.size
training_data = mnist.load_data('data/train_images.idx3-ubyte',
'data/train_labels.idx1-ubyte', 'rb',
60000, 1)
net = nets.Neural_nets(li)
print 'training...'
net.training_model(training_data, epoch, lrate)
val = np.argmax(net.learning_model(img))
print 'The number is %d.' % (val)
else:
print 'loading...'
li[0] = 784
training_data = mnist.load_data('data/train_images.idx3-ubyte',
'data/train_labels.idx1-ubyte', 'rb',
60000, 1)
test_data = mnist.load_data('data/test_images.idx3-ubyte',
'data/test_labels.idx1-ubyte', 'rb', 10000,
0)
net = nets.Neural_nets(li)
print 'training...'
net.training_model(training_data, epoch, lrate, test_data)
def main():
if RE_TRAIN:
if os.path.exists(CKPT_PATH):
shutil.rmtree(CKPT_PATH)
if os.path.exists(GRAPH_PATH):
shutil.rmtree(GRAPH_PATH)
if not os.path.exists(CKPT_PATH):
os.mkdir(CKPT_PATH)
model = VAE(code_size=CODE_SIZE, ckpt_path=CKPT_PATH)
data = mnist.load_data().train
with tf.Session() as sess:
writer = tf.summary.FileWriter(GRAPH_PATH, sess.graph)
model.train(sess, data, FINAL_STEP, LR, BATCH_SIZE, writer, CKPT_STEP)
writer.close()
def load_data():
(x_train, y_train), (x_test, y_test) = mnist.load_data()
number = 10000
x_train = x_train[0:number]
y_train = y_train[0:number]
x_train = x_train.reshape(number,227*227)
x_test = x_test.reshape(x_test.shape[0],227*227)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
#
y_train = np_utils.to_categorical(y_train,10)
y_test = np_utils.to_categorical(y_test,10)
x_train = x_train
x_test = x_test
#
x_train = x_train/255
x_test = x_test/255
return (x_train,y_train),(x_test,y_test)
def keras_fmin_fnct(space):
'''
Data providing function:
This function is separated from model() so that hyperopt
won't reload data for each evaluation run.
'''
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
nb_classes = 10
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
return X_train, Y_train, X_test, Y_test
def trainSizeModerator(train_set_size):
np.random.seed(1)
Xtrain, ytrain, Xtest, ytest = mnist.load_data() #loading the dataset
train_size = train_set_size
test_size = 10000
Xtrain = Xtrain[0:train_size]
ytrain = ytrain[0:train_size]
Xtest = Xtest[0:test_size]
ytest = ytest[0:test_size]
# Precompute sum of squares term for speed
XtrainSOS = np.sum(
Xtrain**2, axis=1,
keepdims=True) #computes sum of squares of element in each row
XtestSOS = np.sum(
Xtest**2, axis=1,
keepdims=True) #computes sum of squares of element in each row
# fully solution takes too much memory so we will classify in batches
# nbatches must be an even divisor of test_size, increase if you run out of memory
if test_size > 1000:
nbatches = 50
else:
nbatches = 5
batches = np.array_split(np.arange(test_size), nbatches)
ypred = np.zeros_like(ytest)
# Classify
for i in range(nbatches):
dst = sqDistance(
Xtest[batches[i]], Xtrain, XtestSOS[batches[i]], XtrainSOS
) #computes eucledian distance between test and train data
closest = np.argmin(dst, axis=1) #picks index of the minimum distance
ypred[batches[i]] = ytrain[
closest] #assigns the value(label) of the minimum index to the predicted array
# Report
errorRate = (ypred != ytest).mean(
) #taking average of errors between actual and predcited labels
#errorString = 'Error Rate: {:.2f}%\n'.format(100*errorRate)#formatting the output
return errorRate * 100
def plot_error(n):
train_size = n
print("Training size", train_size)
test_size = 10000
Xtrain, ytrain, Xtest, ytest = mnist.load_data()
Xtrain = Xtrain[0:train_size]
ytrain = ytrain[0:train_size]
Xtest = Xtest[0:test_size]
ytest = ytest[0:test_size]
# Precompute sum of squares term for speed
XtrainSOS = np.sum(Xtrain**2, axis=1, keepdims=True)
XtestSOS = np.sum(Xtest**2, axis=1, keepdims=True)
# fully solution takes too much memory so we will classify in batches
# nbatches must be an even divisor of test_size, increase if you run out of memory
if test_size > 1000:
nbatches = 50
else:
nbatches = 5
batches = np.array_split(np.arange(test_size), nbatches)
ypred = np.zeros_like(ytest)
# Classify
for i in range(nbatches):
dst = sqDistance(Xtest[batches[i]], Xtrain, XtestSOS[batches[i]],
XtrainSOS)
closest = np.argmin(dst, axis=1)
ypred[batches[i]] = ytrain[closest]
# Report
errorRate = (ypred != ytest).mean()
print('Error Rate: {:.2f}%\n'.format(100 * errorRate))
error.append(errorRate)
#image plot
#plt.imshow(Xtrain[0].reshape(28, 28), cmap='gray')
# plt.show()
return error
def optical_character_recognition(train_size):
Xtrain, ytrain, Xtest, ytest = mnist.load_data()
# train_size = 10000
test_size = 10000
Xtrain = Xtrain[0:train_size]
ytrain = ytrain[0:train_size]
Xtest = Xtest[0:test_size]
ytest = ytest[0:test_size]
# Precompute sum of squares term for speed
XtrainSOS = np.sum(Xtrain**2, axis=1, keepdims=True)
XtestSOS = np.sum(Xtest**2, axis=1, keepdims=True)
# fully solution takes too much memory so we will classify in batches
# nbatches must be an even divisor of test_size, increase if you run out of memory
if test_size > 1000:
nbatches = 50
else:
nbatches = 5
batches = np.array_split(np.arange(test_size), nbatches)
ypred = np.zeros_like(ytest)
# Classify
for i in range(nbatches):
dst = sqDistance(Xtest[batches[i]], Xtrain, XtestSOS[batches[i]],
XtrainSOS)
closest = np.argmin(dst, axis=1)
ypred[batches[i]] = ytrain[closest]
# Report
errorRate = (ypred != ytest).mean()
print('Error Rate: {:.2f}%\n'.format(100 * errorRate))
return errorRate
def nfold_cross_validation(k, train_size):
n = train_size // k
Xtrain, ytrain, Xtest, ytest = mnist.load_data()
Xtrain = Xtrain[0:train_size]
ytrain = ytrain[0:train_size]
indices = list(range(train_size))
np.random.shuffle(indices)
partitions = [
indices[i * n:(i + 1) * n] for i in range((len(indices) + n - 1) // n)
]
if (n != (train_size / k)): #leave out the last partition
del partitions[-1]
error = 0.0
for i in range(len(partitions)):
validation_idx = partitions[i]
training_idx = list(set(indices) - set(partitions[i]))
X_training, X_validation = Xtrain[training_idx], Xtrain[validation_idx]
y_training, y_validation = ytrain[training_idx], ytrain[validation_idx]
# Precompute sum of squares term for speed
X_training_SOS = np.sum(X_training**2, axis=1, keepdims=True)
X_validation_SOS = np.sum(X_validation**2, axis=1, keepdims=True)
ypred = np.zeros_like(y_validation)
# Classify
dst = sqDistance(X_validation, X_training, X_validation_SOS,
X_training_SOS)
closest = np.argmin(dst, axis=1)
ypred = y_training[closest]
# Report
errorRate = (ypred != y_validation).mean()
error = error + errorRate
return (error / len(partitions))
def sqDistance(p, q, pSOS, qSOS):
# Efficiently compute squared euclidean distances between sets of vectors
# Compute the squared Euclidean distances between every d-dimensional point
# in p to every d-dimensional point in q. Both p and q are
# npoints-by-ndimensions.
# d(i, j) = sum((p(i, :) - q(j, :)).^2)
d = np.add(pSOS, qSOS.T) - 2 * np.dot(p, q.T)
return d
np.random.seed(1)
# Set training & testing
Xtrain, ytrain, Xtest, ytest = mnist.load_data()
#train_size = 10000
test_size = 10000
train_sample_size = [100, 1000, 2500, 5000, 7500, 10000]
n_folds = [3, 10, 50, 100, 1000]
error_rate_sample = []
error_rate_n_fold = []
error_n_fold_means = []
Xtest = Xtest[0:test_size]
ytest = ytest[0:test_size]
# Precompute sum of squares term for speed
#XtrainSOS = np.sum(Xtrain**2, axis=1, keepdims=True)
import numpy
import mnist as mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import np_utils
# make seed for cycle results
numpy.random.seed(42)
# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# resize data
X_train = X_train.reshape(60000, 784)
# normalize data
X_train = X_train.astype('float32')
X_train /= 255
# transform tags to categories
y_train = np_utils.to_categorical(y_train, 10)
# 0 -> [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
# 2 -> [0, 0, 1, 0, 0, 0, 0, 0, 0, 0] # only the right element == 1
# create sequential model
model = Sequential()
# add network levels
model.add(Dense(800, input_dim=784, init="normal", activation="relu"))
model.add(Dense(10, init="normal", activation="softmax"))
"""Train a CNN model on MNIST."""
from __future__ import print_function
import keras
from keras.utils import np_utils
import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
# training specific hyperparameters
batch_size = 128
epochs = 1
# Load the data, shuffled and split between train and test sets
data = mnist.load_data({'dataset': {}})
x_train = data['x_train']
y_train = data['y_train']
x_test = data['x_test']
y_test = data['y_test']
# Bring data into necessary format
x_train = mnist.preprocess(x_train, subtact_mean=False)
x_test = mnist.preprocess(x_test, subtact_mean=False)
y_train = np_utils.to_categorical(y_train, mnist.n_classes)
y_test = np_utils.to_categorical(y_test, mnist.n_classes)
# Define model
input_shape = (mnist.img_rows, mnist.img_cols, 1)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu',
from numpyDNN.losses import *
# set hyper parameters
input_size = 28 * 28
output_size = 10
n_hid1 = 100
n_hid2 = 100
n_hid3 = 100
batch_size = 100
learning_rate = 0.01
training_epoch_num = 50
# load mnist data
train_x, train_y = mnist.load_data("mnist_train.csv", one_hot=True)
test_x, test_y = mnist.load_data("mnist_test.csv", one_hot=True)
# build model
model = Model([
Affine(inputs=input_size, outputs=n_hid1, dtype=np.float32),
Sigmoid(inputs=n_hid1, dtype=np.float32),
Affine(inputs=n_hid1, outputs=n_hid2, dtype=np.float32),
Sigmoid(inputs=n_hid1, dtype=np.float32),
Affine(inputs=n_hid2, outputs=n_hid3, dtype=np.float32),
Sigmoid(inputs=n_hid3, dtype=np.float32),
Affine(inputs=n_hid3, outputs=output_size, dtype=np.float32),
Softmax(inputs=output_size, dtype=np.float32)
])
# compile model
def feed_forward(X, weights):
a = [X]
for w in weights:
a.append(np.maximum(a[-1].dot(w), 0))
return a
def grads(X, Y, weights):
grads = np.empty_like(weights)
a = feed_forward(X, weights)
delta = a[-1] - Y
grads[-1] = a[-2].T.dot(delta)
for i in xrange(len(a) - 2, 0, -1):
delta = (a[i] > 0) * delta.dot(weights[i].T)
grads[i - 1] = a[i - 1].T.dot(delta)
return grads / len(X)
trX, trY, teX, teY = mnist.load_data()
weights = [np.random.randn(*w) * 0.1 for w in [(784, 100), (100, 10)]]
num_epochs, batch_size, learn_rate = 30, 20, 0.1
for i in xrange(num_epochs):
for j in xrange(0, len(trX), batch_size):
X, Y = trX[j:j + batch_size], trY[j:j + batch_size]
weights -= learn_rate * grads(X, Y, weights)
prediction = np.argmax(feed_forward(teX, weights)[-1], axis=1)
print i, np.mean(prediction == np.argmax(teY, axis=1))
import tensorflow as tf
import numpy as np
import mnist
def init_weights(shape):
return tf.Variable(tf.random_normal(shape, stddev=0.01))
def feed_forward(X, w_h, w_o):
h = tf.nn.sigmoid(tf.matmul(X, w_h))
return tf.matmul(h, w_o)
(trX, trY), _, (teX, teY) = mnist.load_data(one_hot=True)
w_h, w_o = init_weights([784, 100]), init_weights([100, 10])
num_epochs, batch_size, learn_rate = 30, 10, 0.2
X = tf.placeholder("float", [None, 784])
Y = tf.placeholder("float", [None, 10])
out = feed_forward(X, w_h, w_o)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(out, Y))
train = tf.train.GradientDescentOptimizer(learn_rate).minimize(cost)
sess = tf.Session()
sess.run(tf.initialize_all_variables())
for i in range(num_epochs):
for j in xrange(0, len(trX), batch_size):
batch_x, batch_y = trX[j:j+batch_size], trY[j:j+batch_size]
sess.run(train, feed_dict={X: batch_x, Y: batch_y})
prediction = sess.run(tf.argmax(out, 1), feed_dict={X: teX, Y: teY})
print i, np.mean(prediction == np.argmax(teY, axis=1))
def feed_forward(X, weights):
a = [X]
for w in weights:
a.append(np.maximum(a[-1].dot(w), 0))
return a
def grads(X, Y, weights):
grads = np.empty_like(weights)
a = feed_forward(X, weights)
delta = a[-1] - Y
grads[-1] = a[-2].T.dot(delta)
for i in xrange(len(a) - 2, 0, -1):
delta = (a[i] > 0) * delta.dot(weights[i].T)
grads[i - 1] = a[i - 1].T.dot(delta)
return grads / len(X)
trX, trY, teX, teY = mnist.load_data()
weights = [np.random.randn(*w) * 0.1 for w in [(784, 100), (100, 10)]]
num_epochs, batch_size, learn_rate = 30, 20, 0.1
for i in xrange(num_epochs):
for j in xrange(0, len(trX), batch_size):
X, Y = trX[j : j + batch_size], trY[j : j + batch_size]
weights -= learn_rate * grads(X, Y, weights)
prediction = np.argmax(feed_forward(teX, weights)[-1], axis=1)
print i, np.mean(prediction == np.argmax(teY, axis=1))
def create_cnn(input_shape, n_outputs):
model = keras.models.Sequential()
model.add(keras.layers.InputLayer(input_shape))
model.add(keras.layers.Conv2D(16, (7, 7), activation='relu'))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(n_outputs, activation='softmax'))
return model
(train_images,
train_labels), (val_images,
val_labels), (test_images,
test_labels) = mnist.load_data(nval=1000)
cnn = create_cnn(input_shape=(28, 28, 1), n_outputs=10)
sgd = keras.optimizers.SGD(lr=1e-2)
cnn.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
logs = cnn.fit(train_images,
train_labels,
batch_size=32,
epochs=20,
verbose=1,
validation_data=(val_images, val_labels))
import theano.tensor as T
import numpy as np
import mnist
def init_weights(n_in, n_out):
weights = np.random.randn(n_in, n_out) / np.sqrt(n_in)
return theano.shared(np.asarray(weights, dtype=theano.config.floatX))
def feed_forward(X, w_h, w_o):
h = T.nnet.sigmoid(T.dot(X, w_h))
return T.nnet.softmax(T.dot(h, w_o))
trX, trY, teX, teY = mnist.load_data(one_hot=True)
w_h, w_o = init_weights(28 * 28, 100), init_weights(100, 10)
num_epochs, batch_size, learn_rate = 30, 10, 0.2
X, Y = T.fmatrices('X', 'Y')
y_ = feed_forward(X, w_h, w_o)
weights = [w_h, w_o]
grads = T.grad(cost=T.nnet.categorical_crossentropy(y_, Y).mean(), wrt=weights)
train = theano.function(inputs=[X, Y],
updates=[[w, w - g * learn_rate]
for w, g in zip(weights, grads)],
allow_input_downcast=True)
predict = theano.function(inputs=[X], outputs=T.argmax(y_, axis=1))
#!/usr/bin/env python
"""Train MNIST classifier."""
import mnist
import numpy as np
import tensorflow as tf
# training specific hyperparameters
batch_size = 128
epochs = 1
# Load the data, shuffled and split between train and test sets
data = mnist.load_data({'dataset': {}})
x_train = data['x_train']
y_train = data['y_train']
x_test = data['x_test']
y_test = data['y_test']
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=1024)]
# Build 3 layer DNN with 10, 20, 10 units respectively.
classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
hidden_units=[10, 20, 10],
n_classes=3,
model_dir="/tmp/iris_model")
# Define the training inputs
def get_train_inputs():
x = tf.constant(x_train)
y = tf.constant(y_train)
import numpy as np from keras.layers import Input from keras.models import Model from keras.optimizers import Adam import mnist import matplotlib.pyplot as plt import keras.backend.tensorflow_backend as KTF from gan import build_generator, build_discriminator, plot_images, make_trainable, get_session log_dir = "." KTF.set_session(get_session( )) # Allows 2 jobs per GPU, Please do not change this during the tutorial # prepare MNIST dataset data = mnist.load_data() X_train = data.train_images.reshape(-1, 28, 28, 1) / 255. X_test = data.test_images.reshape(-1, 28, 28, 1) / 255. # plot some real images idx = np.random.choice(len(X_train), 16) plot_images(X_train[idx], fname=log_dir + '/real_images.png') # -------------------------------------------------- # Set up generator, discriminator and GAN (stacked generator + discriminator) # Feel free to modify eg. : # - the provided models (see gan.py) # - the learning rate # - the batchsize # -------------------------------------------------- # Set up generator
def test_mnist_load():
mnist.load_data("mnist")
import minimalist_nn
import numpy as np
import mnist
trX, trY, teX, teY = mnist.load_data(flatten=True)
weights = [
np.random.randn(*w) * 0.1 for w in [(784, 100), (100, 100), (100, 10)]
]
minimalist_nn.train(trX, trY, teX, teY, weights)
def __init__(self, learning_rate):
self.trX, self.trY, self.teX, self.teY = mnist.load_data(one_hot=False,
flatten=False)
self.NCLASSES = 10
self.w = None
self.learning_rate = learning_rate
def compute_accuracy(X_test, Y_test, parameters):
m = Y_test.shape[1]
AL, _ = forward_propagation(X_test, parameters)
Yp = AL.argmax(axis=0).reshape(1, -1)
p = np.sum(Yp == Y_test) / m
return p
########### start of the program ##############
output_channel = 10
num_iteration = 2000
#load data and formalize them
X, Y = mnist.load_data("training")
XX, YY = mnist.load_data("testing")
X_train = X[0:30000, :, :]
Y_train = Y[:, 0:30000]
X_test = XX[0:5000, :, :]
Y_test = YY[:, 0:5000]
X_train_flatten = X_train.reshape(X_train.shape[0], -1).T
X_test_flatten = X_test.reshape(X_test.shape[0], -1).T
Y_train_all = one_to_all_convertion(Y_train, output_channel)
#finalize layers size
img_size = X_train_flatten.shape[0] #28*28=784
hidden_layer_1 = 10
hidden_layer_2 = 5
def run(hyperparams):
##########################################
# Your DL start here. See mnist_mlp.py #
##########################################
'''Trains a simple deep NN on the MNIST dataset.
Gets to 98.40% test accuracy after 20 epochs
(there is *a lot* of margin for parameter tuning).
2 seconds per epoch on a K520 GPU.
'''
# from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop
batch_size = hyperparams['batch_size']
num_classes = 10
epochs = hyperparams['epochs']
activation = hyperparams['activation']
optimizer = hyperparams['optimizer']
# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Dense(512, activation=activation, input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation=activation))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
history = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
##########################################
# End of mnist_mlp.py ####################
##########################################
return history
import tensorflow as tf
import numpy as np
import os
import mnist # get data from
import matplotlib.pyplot as plt # grph
from keras.models import Sequential # ann architecture
from keras.layers import Dense # Layer in ann
from keras.utils import to_categorical
from keras.models import load_model
# load dataset from mnist
mnist = tf.keras.datasets.mnist
# normize the images
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images, test_images = (train_images / 255) - 0.5, (test_images / 255) - 0.5
train_images = train_images.reshape((-1, 784))
test_images = test_images.reshape((-1, 784))
#Defining the model
def baselineK_model():
# create model
model = Sequential()
model.add( Dense(64, activation='relu', input_dim=784))
model.add( Dense(64, activation='relu'))
model.add( Dense(10, activation='softmax'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
import mnist from keras.models import Sequential from keras.layers.core import Dense, Activation, Flatten from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D from keras.layers.normalization import BatchNormalization from keras.optimizers import SGD trX, trY, teX, teY = mnist.load_data(one_hot=True, reshape=(-1, 1, 28, 28)) model = Sequential() model.add(ZeroPadding2D((2, 2), input_shape=trX.shape[1:])) model.add(Convolution2D(16, 3, 3, border_mode='same', activation='relu')) model.add(BatchNormalization(axis=1)) model.add(Convolution2D(16, 3, 3, border_mode='same', activation='relu')) model.add(BatchNormalization(axis=1)) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Convolution2D(32, 3, 3, border_mode='same', activation='relu')) model.add(BatchNormalization(axis=1)) model.add(Convolution2D(32, 3, 3, border_mode='same', activation='relu')) model.add(BatchNormalization(axis=1)) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Convolution2D(64, 3, 3, border_mode='same', activation='relu')) model.add(BatchNormalization(axis=1)) model.add(Convolution2D(64, 3, 3, border_mode='same', activation='relu')) model.add(BatchNormalization(axis=1)) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten())
import numpy as np
import optimise
from nn import NeuralNetwork
import loadData as ld
import mnist
# ds = ld.loadPickle('data/mnist.pkl.gz')
# mnist = ld.loadMNIST('data/mnist.pkl.gz')
mnist = mnist.load_data()
print mnist
# exit()
rng = np.random.RandomState(1111)
nn = NeuralNetwork(rng, 28*28, 10, [500, 500, 500])
optimise.bsgd(nn, mnist)
def test_dots(t, dots):
i = int(t / presentation_time) % len(test_images)
j = np.argmax(dots)
return test_labels[i] == vocab_labels[j]
# --- load the RBM data
rbm_file = 'rbm.npz'
if not os.path.exists(rbm_file):
urllib.urlretrieve("http://files.figshare.com/1448053/rbm.npz", rbm_file)
rbm = np.load(rbm_file)
weights = rbm['weights']
biases = rbm['biases']
# --- load the testing data
[train_set, test_set] = mnist.load_data(train=True, valid=False, test=True)
train_images, train_labels = train_set
test_images, test_labels = test_set
# shuffle
rng = np.random.RandomState(92)
inds = rng.permutation(len(test_images))
test_images = test_images[inds]
test_labels = test_labels[inds]
# --- find average semantic pointers (codes) for each label
train_codes = forward(train_images, weights, biases)
vocab_labels = np.unique(train_labels)
vocab_codes = np.zeros((len(vocab_labels), train_codes.shape[-1]))
for i, label in enumerate(vocab_labels):
vocab_codes[i] = train_codes[train_labels.flatten() == label].mean(0)
import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.optimizers import SGD
trX, trY, teX, teY = mnist.load_data(one_hot=True, reshape=(-1, 1, 28, 28))
model = Sequential()
model.add(Convolution2D(8, 5, 5, input_shape=trX.shape[1:]))
model.add(Activation('sigmoid'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(100))
model.add(Activation('sigmoid'))
model.add(Dense(10))
model.add(Activation('softmax'))
num_epochs, batch_size, learn_rate = 30, 10, 0.2
model.compile(SGD(learn_rate), 'categorical_crossentropy', metrics=['accuracy'])
model.fit(trX, trY, batch_size, num_epochs, verbose=1, validation_data=(teX, teY))
def savenetwork(self,filename):
"""
:return:
"""
with open(filename,"wb") as f:
pickle.dump(self.W,f)
def loadnetwork(self,filename):
with open(filename,"rb") as f:
self.W=pickle.load(f)
if __name__=="__main__":
Mnist=mnist.load_data()
margin,pool=2,4
data,target=mnist.data_process2D(Mnist,margin,pool,"cosine")
Dbond,d,Dout=6,2,10
lamb=0
tnn=tnn_classifier(Dbond,d,Dout)
trainend,testend=40000,50000
train_data,train_target=data[0:trainend],target[0:trainend]
test_data,test_target=data[trainend:testend],target[trainend:testend]
tnn.initialize(train_data.shape[1],train_data.shape[2])
tnn.isometrize()
trainlost,testlost,trainprecision,testprecision=tnn.sweep(train_data,train_target,test_data,test_target,lamb)
tnn.savenetwork("WtrainedWithLinearE.txt")
plt.figure("Precision")
plt.plot(trainprecision)
plt.plot(testprecision)
a.append(sigmoid(a[-1].dot(w)))
return a
def grads(X, Y, weights):
grads = np.empty_like(weights)
a = feed_forward(X, weights)
delta = a[-1] - Y # cross-entropy
grads[-1] = np.dot(a[-2].T, delta)
for i in xrange(len(a)-2, 0, -1):
delta = np.dot(delta, weights[i].T) * d_sigmoid(a[i])
grads[i-1] = np.dot(a[i-1].T, delta)
return grads / len(X)
sigmoid = lambda x: 1 / (1 + np.exp(-x))
d_sigmoid = lambda y: y * (1 - y)
(trX, trY), _, (teX, teY) = mnist.load_data()
trY = mnist.to_one_hot(trY)
weights = [
np.random.randn(784, 100) / np.sqrt(784),
np.random.randn(100, 10) / np.sqrt(100)]
num_epochs, batch_size, learn_rate = 30, 10, 0.2
for i in xrange(num_epochs):
for j in xrange(0, len(trX), batch_size):
X, Y = trX[j:j+batch_size], trY[j:j+batch_size]
weights -= learn_rate * grads(X, Y, weights)
out = feed_forward(teX, weights)[-1]
print i, np.mean(np.argmax(out, axis=1) == teY)
def run(gParameters):
##########################################
# Your DL start here. See mnist_cnn.py #
##########################################
'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''
# from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
batch_size = gParameters['batch_size']
num_classes = 10
epochs = gParameters['epochs']
activation = gParameters['activation']
optimizer = gParameters['optimizer']
# input image dimensions
img_rows, img_cols = 28, 28
# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(
Conv2D(32,
kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.summary()
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
history = model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
##########################################
# End of mnist_mlp.py ####################
##########################################
return history
for w in weights:
a.append(sigmoid(a[-1].dot(w)))
return a
def grads(X, Y, weights):
grads = np.empty_like(weights)
a = feed_forward(X, weights)
delta = a[-1] - Y # cross-entropy
grads[-1] = np.dot(a[-2].T, delta)
for i in xrange(len(a)-2, 0, -1):
delta = np.dot(delta, weights[i].T) * d_sigmoid(a[i])
grads[i-1] = np.dot(a[i-1].T, delta)
return grads / len(X)
sigmoid = lambda x: 1 / (1 + np.exp(-x))
d_sigmoid = lambda y: y * (1 - y)
trX, trY, teX, teY = mnist.load_data(one_hot=True)
weights = [
np.random.randn(28*28, 100) / np.sqrt(28*28),
np.random.randn(100, 10) / np.sqrt(100)]
num_epochs, batch_size, learn_rate = 30, 10, 0.2
for i in xrange(num_epochs):
for j in xrange(0, len(trX), batch_size):
X, Y = trX[j:j+batch_size], trY[j:j+batch_size]
weights -= learn_rate * grads(X, Y, weights)
prediction = np.argmax(feed_forward(teX, weights)[-1], axis=1)
print i, np.mean(prediction == np.argmax(teY, axis=1))
def generate_false_labels(Y):
"""
Y: shape (num examples, 10)
"""
return np.random.randint(0, NUM_CLASSES, Y.shape[0])
if __name__ == "__main__":
# Load classifier parameters
W = np.load("./params/W.npy")
b = np.load("./params/b.npy")
# Load data
train_X, train_Y, test_X, test_Y = load_data()
# Generate a random batch on *test data*
X, Y = get_batch(test_X, test_Y)
# Perform adversarial attacks; for each of these, you should also keep
# score of the classifier's accuracy during each type of attack to compare
# afterwards
# First compute gradients
grad = gradients(W, b, X, Y)
Y = np.argmax(Y, axis=1)
# 0. original example (not an attack!)
Y_hat_original = np.argmax(forward(W, b, X), axis=1)
# -*- coding: utf-8 -*-
import pickle
import sys
from mnist import load_data
# f = open('mnist.pkl', 'rb')
# train_set, valid_set, test_set = pickle.load(f)
train_set, test_set = load_data()
train_set_x, train_set_y = train_set
i = 7
for y in range(0,28):
for x in range(0,28):
if train_set_x[i][y][x]<0.5:
sys.stdout.write(" ")
elif train_set_x[i][y][x]<0.8:
sys.stdout.write("+")
else:
sys.stdout.write("*")
sys.stdout.write("\n")
print("this is labeled ", train_set_y[i])
output = self.forward_pass(x.reshape(-1, 1))
pred = np.argmax(output)
predictions.append(pred == np.argmax(y))
return np.mean(predictions)
def total_loss(loss_list):
size = loss_list.size
return np.sum(loss_list)/size
if __name__ == "__main__":
#load the data
train_data_size = 6000 # Only a subset of training data is utilized, dont change for submission
test_data_size = 1000
train_data = mnist.load_data("train-images-idx3-ubyte.gz")[0:train_data_size]
train_labels = mnist.load_labels("train-labels-idx1-ubyte.gz")[0:train_data_size]
test_data = mnist.load_data("t10k-images-idx3-ubyte.gz")[train_data_size:train_data_size + test_data_size]
test_labels = mnist.load_labels("t10k-labels-idx1-ubyte.gz")[train_data_size:train_data_size + test_data_size]
y_train = NeuralNetwork.prep_labels(train_labels)
y_test = NeuralNetwork.prep_labels(test_labels)
# Initialise the Neural Network
hparams = {"l_rate": 0.001}
np.random.seed(42)
network_params = NeuralNetwork.init_params(num_hidden=160, input_size=784, output_size=10)
feedforwardnn = NeuralNetwork(hparams, train_data, y_train, test_data, y_test, network_params)
# Train
feedforwardnn.train(train_data, y_train, test_data, y_test, 10)
# Evaluate
om hyperopt import Trials, STATUS_OK, tpe
hyperas import optim
hyperas.distributions import choice, uniform, conditional
keras.datasets import mnist
keras.utils import np_utils
keras.models import Sequential
keras.layers.core import Dense, Dropout, Activation
data():
'''
Data providing function:
This function is separated from model() so that hyperopt
won't reload data for each evaluation run.
'''
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
nb_classes = 10
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
def keras_fmin_fnct(space):
'''
Data providing function:
