def test(depth, p, dataset, num_epochs=200, seed=None):
if seed is None:
seed = 0
np.random.seed(seed)
data = None
if dataset == "mnist":
data = mnist.load().astype(np.float32)
elif dataset == "cifar10":
data = cifar10.load().astype(np.float32)
num_observations, input_dim = data.shape
data_split_index = int(num_observations * 0.9)
training_data_iterator = DataIterator(batch_size, data[:data_split_index],
data[:data_split_index])
validation_data_iterator = DataIterator(batch_size,
data[data_split_index:],
data[data_split_index:])
# make net
net = Network(input_dim, input_dim, hidden_layers=([
1000,
] * depth), p=p)
losses = net.train(training_data_iterator,
validation_data_iterator,
num_epochs=num_epochs)
net.close()
return losses
def create_train_test_split(digits,
n_testimages,
transpose_trainig=True,
transpose_test=False):
# load mnist data
x_train, y_train, x_test, y_test = mnist.load()
# pick the images of the first digit from training and test data
d1_train = pick_digit_from_data(x_train, y_train, digits[0])
d1_test = pick_digit_from_data(x_test, y_test, digits[0])[0:n_testimages]
# pick the images of the second digit from training and test data
d2_train = pick_digit_from_data(x_train, y_train, digits[1])
d2_test = pick_digit_from_data(x_test, y_test, digits[1])[0:n_testimages]
# Transpose the data if parameter are set to True
if transpose_trainig:
d1_train = d1_train.T
d2_train = d2_train.T
if transpose_test:
d1_test = d1_test.T
d2_test = d2_test.T
return d1_train, d1_test, d2_train, d2_test
def process_data(task, train_size=60000, test_size=10000, val_perc=0.1):
'''
Creates the datasets to be used in the logistic regression task.
'''
if task == 'logistic_regression':
excluded = [0, 1, 4, 5, 6, 7, 8, 9]
true_class = 2
X_train, Y_train, X_test, Y_test = mnist.load()
train_data, test_data = (X_train, Y_train), (X_test, Y_test)
train_data, test_data = partition_dataset(train_data, test_data,
train_size, test_size)
for digit in excluded:
train_data, test_data = remove_digit(train_data, test_data, digit)
train_data, val_data = create_validation_set(train_data, val_perc)
train_data = normalize_data(train_data)
val_data = normalize_data(val_data)
test_data = normalize_data(test_data)
train_data = append_ones(train_data)
val_data = append_ones(val_data)
test_data = append_ones(test_data)
if task == 'logistic_regression':
train_data = binary_class(train_data, true_class)
val_data = binary_class(val_data, true_class)
test_data = binary_class(test_data, true_class)
return train_data, val_data, test_data
def load_binary_dataset(class1: int, class2: int):
"""
Loads, prunes and splits the dataset into train, and validation.
"""
train_size = 20000
val_size = 10000
X_train, Y_train, X_val, Y_val = mnist.load()
# First 20000 images from train set
X_train, Y_train = X_train[:train_size], Y_train[:train_size]
# Last 2000 images from test set
X_val, Y_val = X_val[:val_size], Y_val[:val_size]
X_train, Y_train = binary_prune_dataset(
class1, class2, X_train, Y_train
)
X_val, Y_val = binary_prune_dataset(
class1, class2, X_val, Y_val
)
# Reshape to (N, 1)
Y_train = Y_train.reshape(-1, 1)
Y_val = Y_val.reshape(-1, 1)
print(f"Train shape: X: {X_train.shape}, Y: {Y_train.shape}")
print(f"Validation shape: X: {X_val.shape}, Y: {Y_val.shape}")
return X_train, Y_train, X_val, Y_val
def get_mnist():
"""
Load the MNIST data
"""
mnist.init()
x_train, t_train, x_test, t_test = mnist.load()
print("Loaded MNIST data")
return x_train, t_train, x_test, t_test
def main():
training_data, validation_data, test_data = mnist.load()
net = mlp.MLP([784,36,10])
epochs = 500
mini_batch_size = 10
learning_rate = 0.5
lmbda = 5.0
drop_prob = 0.5
net.sgd(training_data, epochs, mini_batch_size, test_data, learning_rate, lmbda, drop_prob)
def main():
training_data, validation_data, test_data = mnist.load()
net = mlp.MLP([784, 36, 10])
epochs = 500
mini_batch_size = 10
learning_rate = 0.5
lmbda = 5.0
drop_prob = 0.5
net.sgd(training_data, epochs, mini_batch_size, test_data, learning_rate,
lmbda, drop_prob)
def load_data():
mnist = {}
mnist["training_images"], mnist["training_labels"], mnist[
"test_images"], mnist["test_labels"] = load()
mnist["training_images"] = mnist["training_images"].reshape(
(60000, 1, 28, 28))
mnist["test_images"] = mnist["test_images"].reshape((10000, 1, 28, 28))
mnist["training_labels"] = one_hot(mnist["training_labels"])
mnist["test_labels"] = one_hot(mnist["test_labels"])
return mnist
def get_mnist(num_train):
x_train, y_train, x_test, y_test = mnist.load()
x_train, y_train = shuffle(x_train, y_train)
x_train, y_train = x_train[:num_train], y_train[:num_train]
y_train = one_hot(y_train, 10)
y_test = one_hot(y_test, 10)
x_train, x_val, y_train, y_val = train_test_split(x_train,
y_train,
test_size=.3)
return x_train, y_train, x_val, y_val, x_test, y_test
def test_feedforward_network_n2048(benchmark):
global training_data
if not training_data:
training_data = mnist.load("train")
net = feedforward.Network([28 * 28, 2048, 10])
@benchmark
def train():
net.stochastic_gradient_descent(training_data,
1000,
epochs=10,
mini_batch_size=10,
learning_rate=3.0)
def init(digit1, digit2, num_train, num_test):
# Download dataset
if not os.path.isfile("mnist.pkl"):
mnist.init()
# Load whole dataset into memory
x_train, t_train, x_test, t_test = mnist.load()
# Subset training data
if num_train > 0:
indices1 = [
i for i, j in enumerate(t_train)
if ((j == digit1) or (j == digit2))
]
x = x_train[indices1, :]
y = t_train[indices1]
y = np.cast[int](y)
y[y == digit1] = -1
y[y == digit2] = 1
ind1 = np.random.choice(np.arange(y.size), num_train)
x = x[ind1, :]
y = y[ind1]
else:
x = None
y = None
# Subset test data
if num_test > 0:
indices2 = [
i for i, j in enumerate(t_test) if ((j == digit1) or (j == digit2))
]
xtest = x_test[indices2, :]
ytest = t_test[indices2]
ytest = np.cast[int](ytest)
ytest[ytest == digit1] = -1
ytest[ytest == digit2] = 1
ind2 = np.random.choice(np.arange(ytest.size), num_test)
xtest = xtest[ind2, :]
ytest = ytest[ind2]
else:
xtest = None
ytest = None
# Return
return (x, y, xtest, ytest)
def format():
traindata, trainlabels, testdata, testlabels = mnist.load()
trainlabels = list(trainlabels)
# label data and scale from (0,255) to (0,1)
trainlabeled = [[], [], [], [], [], [], [], [], [], []]
for i in range(len(traindata)):
trainlabeled[trainlabels[i]].append(traindata[i] / 255)
testlabels = list(testlabels)
# label data and scale from (0,255) to (0,1)
testlabeled = [[], [], [], [], [], [], [], [], [], []]
for i in range(len(testdata)):
testlabeled[testlabels[i]].append(testdata[i] / 255)
return trainlabeled, testlabeled
def main():
if len(sys.argv) == 1:
print("Ejecuta: python3 clustering.py [n] [p].")
return
n_proj = int(sys.argv[1])
p = float(sys.argv[2])
x_train, t_train, _, _ = mnist.load()
n, m = 360, 784
data = np.zeros((n, m), dtype='float')
labels = np.zeros((n, 1), dtype='int')
cnt, idx = 0, 0
while cnt < 300:
if t_train[idx] == 3 or t_train[idx] == 8 or t_train[idx] == 9:
data[cnt, :] = (x_train[idx, :] / LA.norm(x_train[idx, :], 1))
labels[cnt] = t_train[idx]
cnt += 1
idx += 1
while cnt < 360:
if t_train[idx] != 3 and t_train[idx] != 8 and t_train[idx] != 9:
data[cnt, :] = (x_train[idx, :] / LA.norm(x_train[idx, :], 1))
labels[cnt] = t_train[idx]
cnt += 1
idx += 1
W_PCA = optimization.PCA(data, n_proj)
data_proj_PCA = data.dot(W_PCA)
data_mean = np.zeros((data.shape[1]), dtype='float')
for i in range(data.shape[1]):
data_mean[i] = np.mean(data[:, i])
gm = optimization.gen_mean(data, data_mean, p)
_, W_PCAGM = optimization.PCAGM(data, gm, n_proj, W_PCA, p)
data_proj_PCAGM = data.dot(W_PCAGM)
k = 3
prec_1 = make_clustering(data_proj_PCA, labels, 3)
prec_2 = make_clustering(data_proj_PCAGM, labels, 3)
print("Precision de la clasificacion (PCA): ", prec_1)
print("Precision de la clasificacion (PCA GM): ", prec_2)
def load_full_mnist():
"""
Loads and splits the dataset into train, validation and test.
"""
train_size = 20000
test_size = 10000
X_train, Y_train, X_val, Y_val = mnist.load()
# First 20000 images from train set
X_train, Y_train = X_train[:train_size], Y_train[:train_size]
# Last 2000 images from test set
X_val, Y_val = X_val[-test_size:], Y_val[-test_size:]
# Reshape to (N, 1)
Y_train = Y_train.reshape(-1, 1)
Y_val = Y_val.reshape(-1, 1)
print(f"Train shape: X: {X_train.shape}, Y: {Y_train.shape}")
print(f"Validation shape: X: {X_val.shape}, Y: {Y_val.shape}")
return X_train, Y_train, X_val, Y_val
def load_split_tasks(n):
tc = 3000
vc = 1000
nclass = 10
ds, vds = mnist.load(shuffle=False, train_count=tc, val_count=vc)
sds = []
for c in range(nclass):
sds.append((ds[tc * c:tc * (c + 1)], vds[vc * c:vc * (c + 1)]))
np.random.shuffle(sds)
tasks = []
for g in range(0, nclass, n):
cds = sds[g:g + n]
if len(cds) == n:
ct, cv = zip(*cds)
ct = np.array([i for j in ct for i in j])
cv = np.array([i for j in cv for i in j])
print ct.shape, cv.shape
tasks.append((ct, cv))
x, y = map(np.array, zip(*ct))
return tasks
def total_mmv():
traindata, _, _, _ = mnist.load()
scale = []
for t in traindata:
scale.append(t / 255)
mmv_total = {
'mean': np.array([]),
'median': np.array([]),
'var': np.array([]),
'std': np.array([])
}
mmv_total['mean'] = np.mean(scale, axis=0)
mmv_total['median'] = np.median(scale, axis=0)
mmv_total['var'] = np.var(scale, axis=0)
mmv_total['std'] = np.std(scale, axis=0)
return mmv_total
def create_train_test_split(digits,
n_testimages,
transpose_trainig=True,
transpose_test=False):
x_train, y_train, x_test, y_test = mnist.load()
d1_train = pick_digit_from_data(x_train, y_train, digits[0])
d1_test = pick_digit_from_data(x_test, y_test, digits[0])[0:n_testimages]
d2_train = pick_digit_from_data(x_train, y_train, digits[1])
d2_test = pick_digit_from_data(x_test, y_test, digits[1])[0:n_testimages]
if transpose_trainig:
d1_train = d1_train.T
d2_train = d2_train.T
if transpose_test:
d1_test = d1_test.T
d2_test = d2_test.T
return d1_train, d1_test, d2_train, d2_test
def load_full_mnist(val_percentage: float):
"""
Loads and splits the dataset into train, validation and test.
"""
train_size = 20000
test_size = 2000
X_train, Y_train, X_test, Y_test = mnist.load()
# First 20000 images from train set
X_train, Y_train = X_train[:train_size], Y_train[:train_size]
# Last 2000 images from test set
X_test, Y_test = X_test[-test_size:], Y_test[-test_size:]
# Reshape to (N, 1)
Y_train = Y_train.reshape(-1, 1)
Y_test = Y_test.reshape(-1, 1)
X_train, Y_train, X_val, Y_val = train_val_split(X_train, Y_train,
val_percentage)
print(f"Train shape: X: {X_train.shape}, Y: {Y_train.shape}")
print(f"Validation shape: X: {X_val.shape}, Y: {Y_val.shape}")
print(f"Test shape: X: {X_test.shape}, Y: {Y_test.shape}")
return X_train, Y_train, X_val, Y_val, X_test, Y_test
def load_binary_dataset(class1: int, class2: int, val_percentage: float):
"""
Loads, prunes and splits the dataset into train, validation and test.
"""
train_size = 20000
test_size = 2000
X_train, Y_train, X_test, Y_test = mnist.load()
# First 20000 images from train set
X_train, Y_train = X_train[:train_size], Y_train[:train_size]
# Last 2000 images from test set
X_test, Y_test = X_test[-test_size:], Y_test[-test_size:]
X_train, Y_train = binary_prune_dataset(class1, class2, X_train, Y_train)
X_test, Y_test = binary_prune_dataset(class1, class2, X_test, Y_test)
# Reshape to (N, 1)
Y_train = Y_train.reshape(-1, 1)
Y_test = Y_test.reshape(-1, 1)
X_train, Y_train, X_val, Y_val = train_val_split(X_train, Y_train,
val_percentage)
print(f"Train shape: X: {X_train.shape}, Y: {Y_train.shape}")
print(f"Validation shape: X: {X_val.shape}, Y: {Y_val.shape}")
print(f"Test shape: X: {X_test.shape}, Y: {Y_test.shape}")
return X_train, Y_train, X_val, Y_val, X_test, Y_test
def mnist_preprocess(data):
data['data'] /= 255.
return data
# Logger setup
logger = Logger('MNIST AE',
train_log_mode='TRAIN_LOSS_ONLY',
test_log_mode='TEST_LOSS_ONLY')
# Configure GPU Device
if args.gpu >= 0:
cuda.check_cuda_available()
xp = cuda.cupy if args.gpu >= 0 else np
# loading dataset
dataset = mnist.load()
dim = dataset['train']['data'][0].size
N_train = len(dataset['train']['target'])
N_test = len(dataset['test']['target'])
train_data_dict = {'data':dataset['train']['data'].reshape(N_train, dim).astype(np.float32)}
test_data_dict = {'data':dataset['test']['data'].reshape(N_test, dim).astype(np.float32)}
train_data = DataFeeder(train_data_dict, batchsize=args.batch)
test_data = DataFeeder(test_data_dict, batchsize=args.valbatch)
train_data.hook_preprocess(mnist_preprocess)
test_data.hook_preprocess(mnist_preprocess)
# Model Setup
h_units = 1200
model = models.AutoencoderModel(
import mnist import numpy as np import one_hot_encoding as ohe import matplotlib.pyplot as plt import pickle #Data settings training_data_size = 55000 validation_data_size = 4000 testing_data_size = 1000 #Loading data from MNIST dataset X_train, Y_train, X_test, Y_test = mnist.load() #Reducing values to between 0 - 1 X_train = X_train/255 X_test = X_test/255 #Performing the "bias trick" X_train = np.concatenate((X_train,np.ones([60000,1])), axis=1) X_test = np.concatenate((X_test,np.ones([10000,1])), axis=1) #Selecting training data and validation data training_data_input = X_train[0:training_data_size,:].copy() training_data_output = Y_train[0:training_data_size].copy() validation_data_input = X_train[training_data_size:training_data_size+validation_data_size].copy() validation_data_output = Y_train[training_data_size:training_data_size+validation_data_size].copy() testing_data_input = X_test[-testing_data_size:].copy() testing_data_output = Y_test[-testing_data_size:].copy() #One hot encode the wanted results
# goal: use weights/biases learned in rbm_matlab_minst_general.py to get two things:
# 1. the low dimensional representation of each example
# 2. the reconstruction of each example
# this is basically going to be a translation of the non-backprop part in backprop.m
import numpy as np
import mnist
from rbm_matlab_mnist_general import random_mini_batches
from pylab import imshow, cm, show
import os
## load mnist data, make batches
# read data
x_train, t_train, x_test, t_test = mnist.load()
# scale data
x_train = x_train / 255
x_test = x_test / 255
# batch input data
batchdata = random_mini_batches(
x_train, mini_batch_size=100) # list of batches of input data
numbatches = len(batchdata)
## load weights
home = os.getenv('HOME')
vishid = np.load(home + '/Deep_Learning_Examples/RBM/vishid.npy')
hidrecbiases = np.load(home + '/Deep_Learning_Examples/RBM/hidrecbiases.npy')
visbiases = np.load(home + '/Deep_Learning_Examples/RBM/visbiases.npy')
hidpen = np.load(home + '/Deep_Learning_Examples/RBM/hidpen.npy')
penrecbiases = np.load(home + '/Deep_Learning_Examples/RBM/penrecbiases.npy')
# ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
# ax.set_title('Training: %i' % label)
# flatten the images
n_samples = len(digits.images)
data = digits.images.reshape((n_samples, -1))
X_train2, X_test2, y_train2, y_test2 = train_test_split(data,
digits.target,
test_size=0.5,
shuffle=False)
# print(X_train2[1])
# print(y_train2[1])
# print(X_test2[1])
# print(y_test2[1])
X_train, y_train, X_test, y_test = mnist.load()
X_train = (X_train[1:limit, :] / 16.0).astype(numpy.uint8).astype(
numpy.float64)
X_test = (X_test[1:limit, :] / 16.0).astype(numpy.uint8).astype(numpy.float64)
y_train = y_train[1:limit]
y_test = y_test[1:limit]
# print(X_train[2])
# print(y_train[2])
# print(X_test[1])
# print(y_test)
# Create a classifier: a support vector classifier
clf = svm.SVC(gamma='scale')
# Learn the digits on the train subset
def __init__(self):
self.X_train, self.Y_train, self.X_test, self.Y_test = mnist.load()
self.X_val = None
self.Y_val = None
self.X_train_vanilla = self.X_train
self.Y_train_vanilla = self.Y_train
def vectorize(i: int) -> List:
vec = np.zeros((10, 1))
vec[i] = 1
return vec
epochs = 1
mini_batch_size = 20
eta = 0.4
NN_struct = []
input_layer = 784
output_layer = 10
hidden_layers = [100]
NN_struct.append(input_layer)
for layer in hidden_layers:
NN_struct.append(layer)
NN_struct.append(output_layer)
train_images, train_labels, test_images, test_labels = load()
train_labels = np.array([vectorize(i) for i in train_labels])
test_labels = np.array([vectorize(i) for i in test_labels])
train_data = np.array(list(zip(train_images, train_labels)))
test_data = np.array(list(zip(test_images, test_labels)))
network = Network([784, 30, 10])
network.train_SGD(train_data, epochs, mini_batch_size,
eta, test_data=test_data)
print(f"Saving object via pickle with parameters:\nepochs - {epochs}\n"
f"minibatch size - {mini_batch_size}\neta - {eta}")
network.save(f"SNN_{epochs}_{mini_batch_size}_{eta}_HL_{hidden_layers}.pkl")
print("Done.")
import torch.nn.functional as F
import torch.nn as nn
import torch
from torch import optim
import mnist
import numpy as np
trainingData, trainingLabels, testData, testLabels = mnist.load()
trainingData = trainingData / 255 > 0.5
testData = testData / 255 > 0.5
trainingData = trainingData.astype(float)
testData = testData.astype(float)
trainingSize = 60000
actualTrainingSize = 50000
validSize = 10000
testSize = 10000
numEpochs = 10
class regressionClassifier(nn.Module):
def __init__(self):
super(regressionClassifier, self).__init__()
self.output = nn.Linear(784, 10)
def forward(self, x):
x = self.output(x)
print(str(x.size()))
#x = F.softmax(x,dim = 1 )
return x #nn.LogSoftmax(dim1)
# architecture two
shapes = [(28, 28), 1000, 500, 200]
rf_shapes = [(9, 9), None, None]
rates = [1., 1., 1.]
n_layers = len(shapes) - 1
assert len(rf_shapes) == n_layers
assert len(rates) == n_layers
# --- define our rate neuron model
neuron = ('softlif', dict(
sigma=0.01, tau_rc=0.02, tau_ref=0.002, gain=1, bias=1, amp=1. / 63.04))
neuron_fn = neurons.get_theano_fn(*neuron)
# --- load the data
train, valid, test = mnist.load(
normalize=True, shuffle=True, spaun=args.spaun)
train_images, test_images = train[0], test[0]
# --- pretrain with SGD backprop
n_epochs = 15
batch_size = 100
deep = DeepAutoencoder()
data = train_images
for i in range(n_layers):
vis_func = None if i == 0 else neuron_fn
# create autoencoder for the next layer
auto = Autoencoder(
shapes[i], shapes[i+1], rf_shape=rf_shapes[i],
vis_func=vis_func, hid_func=neuron_fn)
# coding: utf-8
import numpy as np
import pickle
import mnist
import func
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from two_layer_net import TwoLayerNet
(x_train, t_train), (x_test, t_test) = mnist.load(normalize=True,
one_hot_label=True)
iters_num = 10000
train_size = x_train.shape[0]
batch_size = 100
learning_rate = 0.1
train_loss_list = []
train_acc_list = []
test_acc_list = []
iter_per_epoch = max(train_size / batch_size, 1)
net = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)
for i in range(iters_num):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
args = parser.parse_args()
if not os.path.exists(args.loadfile):
raise IOError("Cannot find '%s'" % args.loadfile)
data = np.load(args.loadfile)
if all(a in data for a in ['weights', 'biases', 'Wc', 'bc']):
# Static network params file
if 'neuron' in data:
_, neuron_params = data['neuron']
else:
neuron_params = dict(sigma=0.01, tau_rc=0.02, tau_ref=0.002,
gain=1, bias=1, amp=1. / 63.04)
# --- load the testing data
_, _, [images, labels] = mnist.load(
normalize=True, shuffle=True, spaun=args.spaun)
assert np.unique(labels).size == data['bc'].size
# --- compute the error
neuron = ('softlif', dict(neuron_params))
errors = compute_static_error(data, images, labels, neuron)
print("----- Static network with softlif -----")
print("Static error: %0.2f%%" % (100 * errors.mean()))
neuron = ('lif', dict(neuron_params))
neuron[1].pop('sigma')
errors = compute_static_error(data, images, labels, neuron)
print("----- Static network with lif -----")
print("Static error: %0.2f%%" % (100 * errors.mean()))
view_static(data, images, labels, neuron)
random_state=None,
verbose=True,
stopping_criterion=None, # 'edv', 'tie'
edv_threshold=0.25,
tie_threshold=0.25,
#=======================================================================
# sparse=True,
#=======================================================================
sparse=False,
minimum_sparseness=0.25,
maximum_sparseness=0.75,
early_stopping=False,
validation_fraction=0.1,
tol=0.0001,
n_iter_no_change=10,
metric='rmse',
prob_skip_connection=0.0) # 0.35
return estimator
if __name__ == '__main__':
scale = True
X_train, y_train, X_test, y_test = load(scale)
for run in range(1, 1 + 1):
print('MNIST: SLM run', run)
estimator = get_estimator()
fit_and_predict(estimator, X_train, y_train, X_test, y_test)
parser.add_argument('--max_epochs', type=int, default=1000)
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('output_dir', type=str)
parser.add_argument('network_structure', type=str)
args = parser.parse_args()
os.makedirs(args.output_dir)
# Set up the network:
print "Setting up network..."
dimensions = [28*28] + [int(x) for x in args.network_structure.split("x")] + [10]
net = models.BatchTrainedModel(models.perceptron_model(dimensions))
print "Loading MNIST..."
(train_x, train_y), (test_x, test_y) = mnist.load()
print "Done Loading MNIST."
print "%d training examples" % train_x.shape[0]
print "Training..."
graph_f = open("%s/graph.tsv" % args.output_dir, "w")
for i in xrange(args.max_epochs+1):
print "Ran for", i, "epochs"
net.write("%s/epoch_%04d.hdf5" % (args.output_dir, i))
train_error = net.error_rate(train_x, train_y)
test_error = net.error_rate(test_x, test_y)
graph_f.write("%d\t%f\t%f\n" % (i, train_error, test_error))
graph_f.flush()
print "Train Error rate =", train_error
print "Test Error rate =", test_error
import mnist
import numpy as np
import NeuralNetwork as nn
import random
import pygame
import math
# Get data from MNIST
# NOTE: I did not write the code to retrieve the data, code taken from:
# https://github.com/hsjeong5/MNIST-for-Numpy
imgTrain, lblTrain, imgTest, lblTest = mnist.load()
# Initialize input, hidden and output layers
il = np.zeros((784, 1))
hl1 = np.zeros((50, 1))
hl2 = np.zeros((50, 1))
middleLayer = np.zeros((2, 1))
hl3 = np.zeros((50, 1))
hl4 = np.zeros((50, 1))
ol = np.zeros((784, 1))
layers = [il, hl1, middleLayer, hl4, ol]
# Initializes the network, loading the weights and biases from the files
#
network = nn.NeuralNetwork(layers,
learningRate=0.0003,
weightImportFile="EncoderWeights.txt",
biasImportFile="EncoderBiases.txt")
def load_data():
global training_data, test_data
training_data, test_data = mnist.load()
def train(input_dim=INPUT_DIM,
batch_size=BATCH_SIZE,
n_features_first=N_FEATURES_FIRST,
critic_iters=CRITIC_ITERS,
lambda_reg=LAMBDA,
learning_rate=1e-4,
iterations=ITERS,
fixed_noise_size=FIXED_NOISE_SIZE,
n_features_reduction_factor=2,
gen_fix_layer_1=False,
gen_fix_layer_2=False,
gen_fix_layer_3=False,
gen_fix_layer_4=False,
disc_fix_layer_1=False,
disc_fix_layer_2=False,
disc_fix_layer_3=False,
disc_fix_layer_4=False,
architecture='DCGAN',
init_method='He',
BN_layers_trainable=True,
load_saved=True):
"""
- this is the function to use to train a GAN model for MNIST, with the configuration given by the parameters
- the function computes losses and auto-saves the model every 100 steps and automatically resumes training where it
stopped (when load_saved=True)
:param input_dim:
:param batch_size:
:param n_features_first:
:param critic_iters:
:param lambda_reg:
:param learning_rate:
:param iterations:
:param fixed_noise_size:
:param n_features_reduction_factor: integer, e.g.: 1: use same number of feature-maps everywhere, 2: half the number
of feature-maps in every step
:param architecture: right now only supports 'WGANGP' and 'DCGAN', defaults to 'DCGAN'
:param init_method: the method with which the variables are initialized, support: 'uniform', 'normal',
'truncated_normal' (each using std given by xavier initializer), 'normal1', 'truncated_normal1' (each using
std 1), 'normal_BN', 'uniform_BN', 'normal_BN_shift', 'He', defaults to 'He'
:param BN_layers_trainable: shall the BN layers be trainable
:param load_saved:
:return:
"""
# -------------------------------------------------------
# setting for sending emails and getting statistics
send = settings.send_email
get_stats = settings.get_statistics
# -------------------------------------------------------
# architecture default
if architecture not in ['WGANGP']:
architecture = 'DCGAN'
if architecture == 'DCGAN':
lambda_reg = None
# -------------------------------------------------------
# init_method default
if init_method not in [
'normal', 'truncated_normal', 'normal1', 'truncated_normal1',
'normal_BN', 'uniform_BN', 'normal_BN_shift', 'He',
'LayerDistribution'
]:
init_method = 'uniform'
# -------------------------------------------------------
# create unique folder name
dir1 = 'partly_fixed2/'
directory = dir1+str(input_dim)+'_'+str(batch_size)+'_'+str(n_features_first)+'_'+str(critic_iters)+'_'+\
str(lambda_reg)+'_'+str(learning_rate)+'_'+str(n_features_reduction_factor)+'_'+\
str(gen_fix_layer_1)+'_'+str(gen_fix_layer_2)+'_'+str(gen_fix_layer_3)+'_'+str(gen_fix_layer_4)+'_' + \
str(disc_fix_layer_1) + '_' + str(disc_fix_layer_2) + '_' + str(disc_fix_layer_3) + '_' + \
str(disc_fix_layer_4) + '_' + \
str(architecture)+'_'+str(init_method)+'_'+str(BN_layers_trainable)+'_'+str(BN)+'/'
samples_dir = directory + 'samples/'
model_dir = directory + 'model/'
# create directories if they don't exist
if not os.path.isdir(dir1):
call(['mkdir', dir1])
if not os.path.isdir(directory):
load_saved = False
print 'make new directory:', directory
print
call(['mkdir', directory])
call(['mkdir', samples_dir])
call(['mkdir', model_dir])
# if directories already exist, but model wasn't saved so far, set load_saved to False
if 'training_progress.csv' not in os.listdir(directory):
load_saved = False
# -------------------------------------------------------
# initialize a TF session
config = tf.ConfigProto()
if N_CPUS_TF is None:
number_cpus_tf = settings.number_cpus
else:
number_cpus_tf = N_CPUS_TF
config.intra_op_parallelism_threads = number_cpus_tf
config.inter_op_parallelism_threads = number_cpus_tf
session = tf.Session(config=config)
# -------------------------------------------------------
# convenience function to build the model
def build_model(gen_fix_layer_1_b=gen_fix_layer_1,
gen_fix_layer_2_b=gen_fix_layer_2,
gen_fix_layer_3_b=gen_fix_layer_3,
gen_fix_layer_4_b=gen_fix_layer_4,
disc_fix_layer_1_b=disc_fix_layer_1,
disc_fix_layer_2_b=disc_fix_layer_2,
disc_fix_layer_3_b=disc_fix_layer_3,
disc_fix_layer_4_b=disc_fix_layer_4):
with tf.name_scope('placeholders'):
x_true = tf.placeholder(tf.float32, [None, 28, 28, 1])
z = tf.placeholder(tf.float32, [None, input_dim])
x_generated = generator(
z,
n_features_first=n_features_first,
n_features_reduction_factor=n_features_reduction_factor,
fix_layer_1=gen_fix_layer_1_b,
fix_layer_2=gen_fix_layer_2_b,
fix_layer_3=gen_fix_layer_3_b,
fix_layer_4=gen_fix_layer_4_b,
architecture=architecture,
init_method=init_method)
d_true = discriminator(
x_true,
reuse=False,
n_features_first=n_features_first,
n_features_reduction_factor=n_features_reduction_factor,
fix_layer_1=disc_fix_layer_1_b,
fix_layer_2=disc_fix_layer_2_b,
fix_layer_3=disc_fix_layer_3_b,
fix_layer_4=disc_fix_layer_4_b,
architecture=architecture,
init_method=init_method)
d_generated = discriminator(
x_generated,
reuse=True,
n_features_first=n_features_first,
n_features_reduction_factor=n_features_reduction_factor,
fix_layer_1=disc_fix_layer_1_b,
fix_layer_2=disc_fix_layer_2_b,
fix_layer_3=disc_fix_layer_3_b,
fix_layer_4=disc_fix_layer_4_b,
architecture=architecture,
init_method=init_method)
if architecture == 'DCGAN':
with tf.name_scope('loss'):
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_generated, labels=tf.ones_like(d_generated)))
d_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_generated,
labels=tf.zeros_like(d_generated))) +\
tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_true,
labels=tf.ones_like(d_true)))
d_loss = d_loss / 2.
with tf.name_scope('g_optimizer'):
g_optimizer = tf.train.AdamOptimizer(learning_rate=2 *
learning_rate,
beta1=0.5)
g_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='generator')
# make BN layers trainable or not, depending on BN_layers_trainable
g_vars2 = []
not_to_include = []
if gen_fix_layer_1_b:
not_to_include += [
'generator/fully_connected/BatchNorm/beta:0'
]
if gen_fix_layer_2_b:
not_to_include += [
'generator/Conv2d_transpose/BatchNorm/beta:0'
]
if gen_fix_layer_3_b:
not_to_include += [
'generator/Conv2d_transpose_1/BatchNorm/beta:0'
]
if disc_fix_layer_1_b:
not_to_include += ['discriminator/Conv/BatchNorm/beta:0']
if disc_fix_layer_2_b:
not_to_include += ['discriminator/Conv_1/BatchNorm/beta:0']
if disc_fix_layer_3_b:
not_to_include += ['discriminator/Conv_2/BatchNorm/beta:0']
for v in g_vars:
if v.name not in not_to_include:
g_vars2 += [v]
if not BN_layers_trainable:
g_vars = g_vars2
g_train = g_optimizer.minimize(g_loss, var_list=g_vars)
with tf.name_scope('d_optimizer'):
d_optimizer = tf.train.AdamOptimizer(learning_rate=2 *
learning_rate,
beta1=0.5)
d_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='discriminator')
# make BN layers trainable or not, depending on BN_layers_trainable
d_vars2 = []
for v in d_vars:
if v.name not in not_to_include:
d_vars2 += [v]
if not BN_layers_trainable:
d_vars = d_vars2
d_train = d_optimizer.minimize(d_loss, var_list=d_vars)
else: # WGAN-GP
with tf.name_scope('regularizer'):
epsilon = tf.random_uniform([batch_size, 1, 1, 1], 0.0, 1.0)
x_hat = epsilon * x_true + (1 - epsilon) * x_generated
d_hat = discriminator(
x_hat,
reuse=True,
n_features_first=n_features_first,
n_features_reduction_factor=n_features_reduction_factor,
fix_layer_1=disc_fix_layer_1_b,
fix_layer_2=disc_fix_layer_2_b,
fix_layer_3=disc_fix_layer_3_b,
fix_layer_4=disc_fix_layer_4_b,
architecture=architecture,
init_method=init_method)
gradients = tf.gradients(d_hat, x_hat)[0]
ddx = tf.sqrt(tf.reduce_sum(gradients**2, axis=[1, 2]))
d_regularizer = tf.reduce_mean((ddx - 1.0)**2)
with tf.name_scope('loss'):
g_loss = -tf.reduce_mean(d_generated)
wasserstein_dist = tf.reduce_mean(d_true) - tf.reduce_mean(
d_generated)
d_loss = -wasserstein_dist + lambda_reg * d_regularizer
with tf.name_scope('g_optimizer'):
g_optimizer = tf.train.AdamOptimizer(
learning_rate=learning_rate, beta1=0, beta2=0.9)
g_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='generator')
g_train = g_optimizer.minimize(g_loss, var_list=g_vars)
with tf.name_scope('d_optimizer'):
d_optimizer = tf.train.AdamOptimizer(
learning_rate=learning_rate, beta1=0, beta2=0.9)
d_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='discriminator')
d_train = d_optimizer.minimize(d_loss, var_list=d_vars)
# initialize variables using uniform xavier init method, see tensorflow documentation
session.run(tf.global_variables_initializer())
if architecture == 'DCGAN':
return x_true, z, x_generated, g_loss, d_loss, g_train, d_train, g_vars, d_vars
else: # WGANGP
return x_true, z, x_generated, g_loss, wasserstein_dist, d_loss, g_train, d_train, g_vars, d_vars
# -------------------------------------------------------
# build the model
if (init_method in ['uniform', 'He', 'normal']) or load_saved:
if architecture == 'DCGAN':
x_true, z, x_generated, g_loss, d_loss, g_train, d_train, g_vars, d_vars = build_model(
)
else: # WGANGP
x_true, z, x_generated, g_loss, wasserstein_dist, d_loss, g_train, d_train, g_vars, d_vars = build_model(
)
else: # not load_saved and not 'uniform'
# build model with all variables trainable to be able to change weights
if architecture == 'DCGAN':
x_true, z, x_generated, g_loss, d_loss, g_train, d_train, \
g_vars, d_vars = build_model(False, False, False, False,False, False, False, False)
else: # WGANGP
x_true, z, x_generated, g_loss, wasserstein_dist, d_loss, g_train, \
d_train, g_vars, d_vars = build_model(False, False, False, False, False, False, False, False)
# change the weights as wanted
saver = tf.train.Saver(max_to_keep=1)
trainable_vars = tf.trainable_variables()
if get_stats:
import matplotlib.pyplot as plt
for v in trainable_vars:
print 'change weights of: ' + str(v.name)
weights = session.run(v)
# if 'BatchNorm' in v.name: #delete
# print 'BN weights:' #delete
# print weights #delete
# print #delete
if init_method == 'truncated_normal': # using xavier init method, see tensorflow documentation
max_abs_val = np.max(np.abs(weights))
session.run(
tf.assign(v,
value=tf.truncated_normal(v.shape,
mean=0.0,
stddev=max_abs_val /
np.sqrt(3))))
elif init_method == 'normal1':
session.run(
tf.assign(v,
value=tf.random_normal(v.shape,
mean=0.0,
stddev=1.0)))
elif init_method == 'truncated_normal1':
session.run(
tf.assign(v,
value=tf.truncated_normal(v.shape,
mean=0.0,
stddev=1.0)))
elif init_method == 'uniform_BN':
max_abs_val = np.max(np.abs(weights))
if 'BatchNorm' in v.name:
session.run(
tf.assign(v,
value=tf.random_uniform(v.shape,
minval=-last_val,
maxval=last_val)))
last_val = max_abs_val
elif init_method == 'normal_BN':
max_abs_val = np.max(np.abs(weights))
if 'BatchNorm' in v.name:
session.run(
tf.assign(v,
value=tf.random_normal(v.shape,
mean=0.0,
stddev=last_val /
np.sqrt(3))))
else:
session.run(
tf.assign(v,
value=tf.random_normal(v.shape,
mean=0.0,
stddev=max_abs_val /
np.sqrt(3))))
last_val = max_abs_val
elif init_method == 'normal_BN_shift':
max_abs_val = np.max(np.abs(weights))
if 'BatchNorm' in v.name:
session.run(
tf.assign(v,
value=tf.random_normal(v.shape,
mean=-last_val,
stddev=last_val)))
else:
session.run(
tf.assign(v,
value=tf.random_normal(v.shape,
mean=0.0,
stddev=max_abs_val /
np.sqrt(3))))
last_val = max_abs_val
elif init_method == 'LayerDistribution':
if v.name == 'generator/fully_connected/weights:0':
session.run(
tf.assign(v,
value=tf.random_normal(v.shape,
mean=0.0,
stddev=0.037907723)))
elif v.name == 'generator/Conv2d_transpose/weights:0':
session.run(
tf.assign(v,
value=tf.random_normal(v.shape,
mean=-0.007851141,
stddev=0.034838371)))
elif v.name == 'generator/Conv2d_transpose_1/weights:0':
session.run(
tf.assign(v,
value=tf.random_normal(v.shape,
mean=-0.001966879,
stddev=0.037020162)))
elif v.name == 'generator/Conv2d_transpose_2/weights:0':
session.run(
tf.assign(v,
value=tf.random_normal(v.shape,
mean=-0.121885814,
stddev=0.294095486)))
elif v.name == 'discriminator/Conv/weights:0':
session.run(
tf.assign(v,
value=tf.random_normal(v.shape,
mean=-0.005809855,
stddev=0.044240803)))
elif v.name == 'discriminator/Conv_1/weights:0':
session.run(
tf.assign(v,
value=tf.random_normal(v.shape,
mean=-0.000329115,
stddev=0.03293338)))
elif v.name == 'discriminator/Conv_2/weights:0':
session.run(
tf.assign(v,
value=tf.random_normal(v.shape,
mean=-0.000697783,
stddev=0.028810507)))
elif v.name == 'discriminator/fully_connected/weights:0':
session.run(
tf.assign(v,
value=tf.random_normal(v.shape,
mean=0.000849896,
stddev=0.074863143)))
if get_stats:
weights_new = session.run(v)
f = plt.figure()
plt.hist(np.reshape(weights_new, newshape=(-1, )),
bins=100,
density=True)
f.savefig(fname=directory +
v.name.replace('/', '_').replace(':', '') + '.png')
plt.close(f)
saver.save(sess=session, save_path=model_dir + 'saved_model')
print
print 'weights were initialized with: ' + init_method
print
# load new session, so that no conflict with names in the name_scopes
session.close()
tf.reset_default_graph()
session = tf.Session(config=config)
# load the model with the perturbed weights, but now s.t. the correct variables are trainable
if architecture == 'DCGAN':
x_true, z, x_generated, g_loss, d_loss, g_train, d_train, g_vars, d_vars = build_model(
)
else: # WGANGP
x_true, z, x_generated, g_loss, wasserstein_dist, d_loss, g_train, d_train, g_vars, d_vars = build_model(
)
# restore the model with the correctly initialized weights
saver = tf.train.Saver(max_to_keep=1)
saver.restore(sess=session, save_path=model_dir + 'saved_model')
print 'loaded model with weights initialized with: ' + init_method
print
# -------------------------------------------------------
# FK: For saving samples, taken from IWGAN
fixed_noise = np.random.normal(size=(fixed_noise_size,
input_dim)).astype('float32')
def generate_image(frame):
samples = session.run(x_generated, feed_dict={
z: fixed_noise
}).squeeze()
# print samples.shape
save_images.save_images(samples.reshape((fixed_noise_size, 28, 28)),
samples_dir + 'iteration_{}.png'.format(frame))
# -------------------------------------------------------
# FK: for saving the model create a saver
saver = tf.train.Saver(max_to_keep=1)
iterations_trained = 0
if architecture == 'DCGAN':
training_progress = pd.DataFrame(
data=None, index=None, columns=['iteration', 'time', 'd_loss'])
else: # WGAN-GP
training_progress = pd.DataFrame(
data=None,
index=None,
columns=['iteration', 'time', 'Wasserstein_dist', 'd_loss'])
# restore the model:
if load_saved:
saver.restore(sess=session, save_path=model_dir + 'saved_model')
iterations_trained = int(np.loadtxt(fname=model_dir +
'iterations.csv'))
tp_app = pd.read_csv(filepath_or_buffer=directory +
'training_progress.csv',
index_col=0,
header=0)
training_progress = pd.concat([training_progress, tp_app],
axis=0,
ignore_index=True)
print 'loaded training progress, and the model, which was already trained for {} iterations'.format(
iterations_trained)
print training_progress
print
# if the network is already trained completely, set send to false
if iterations_trained == iterations:
send = False
# -------------------------------------------------------
# FK: print and get model summary
n_params_gen = model_summary(var_list=g_vars)[0]
print
n_params_disc = model_summary(var_list=d_vars)[0]
print
# -------------------------------------------------------
# FK: print model config to file
model_config = [[
'input_dim', 'batch_size', 'n_features_first', 'critic_iters',
'lambda_reg', 'learning_rate', 'fixed_noise_size',
'n_features_reduction_factor', 'gen_fix_layer_1', 'gen_fix_layer_2',
'gen_fix_layer_3', 'gen_fix_layer_4', 'disc_fix_layer_1',
'disc_fix_layer_2', 'disc_fix_layer_3', 'disc_fix_layer_4',
'architecture', 'init_method', 'BN_layers_trainable',
'n_trainable_params_gen', 'n_trainable_params_disc'
],
[
input_dim, batch_size, n_features_first, critic_iters,
lambda_reg, learning_rate, fixed_noise_size,
n_features_reduction_factor, gen_fix_layer_1,
gen_fix_layer_2, gen_fix_layer_3, gen_fix_layer_4,
disc_fix_layer_1, disc_fix_layer_2, disc_fix_layer_3,
disc_fix_layer_4, architecture, init_method,
BN_layers_trainable, n_params_gen, n_params_disc
]]
model_config = np.transpose(model_config)
model_config = pd.DataFrame(data=model_config)
model_config.to_csv(path_or_buf=directory + 'model_config.csv')
print 'saved model configuration'
print
# -------------------------------------------------------
# FK: get the MNIST data loader
train_gen, dev_gen, test_gen = mnist.load(batch_size, batch_size)
# create an infinite generator
def inf_train_gen():
while True:
for images, targets in train_gen():
yield images
gen = inf_train_gen()
# -------------------------------------------------------
# training loop
print model_config
print
t = time.time() # get start time
# for average times:
if get_stats:
t1s = np.zeros((iterations - iterations_trained))
t2s = np.zeros((iterations - iterations_trained))
t3s = np.zeros((iterations - iterations_trained))
t4s = np.zeros((iterations - iterations_trained))
for i in xrange(iterations - iterations_trained):
z_train = np.random.randn(batch_size, input_dim)
if get_stats:
tt1 = time.time()
session.run(g_train, feed_dict={z: z_train})
if get_stats:
tt1 = time.time() - tt1
# loop for critic training
for j in xrange(critic_iters):
# FK: insert the following 3 lines s.t. not the same batch is used for all 5 discriminator updates
if get_stats:
tt = time.time()
batch = gen.next()
images = batch.reshape([-1, 28, 28, 1])
z_train = np.random.randn(batch_size, input_dim)
if get_stats:
print '\ncomputation time to get true batch and random vector: {}'.format(
time.time() - tt)
tt = time.time()
session.run(d_train, feed_dict={x_true: images, z: z_train})
if get_stats:
t1 = time.time() - tt + tt1
t1s[i] = t1
print 'computation time to train for 1 iteration (minimize disc and gen one step): t1 = {}'.format(
t1)
tt = time.time()
session.run(d_loss, feed_dict={x_true: images, z: z_train})
session.run(g_loss, feed_dict={z: z_train})
t2 = time.time() - tt
t2s[i] = t2
print 'computation time to compute the disc. and gen. loss once: t2 = {}'.format(
t2)
tt = time.time()
session.run(x_generated, feed_dict={z: z_train})
t3 = time.time() - tt
t3s[i] = t3
print 'computation time to compute x_generated: t3 = {}'.format(
t3)
print 't1/t2 = {}'.format(t1 / t2)
# list_ = session.run(g_optimizer.compute_gradients(g_loss, var_list=g_vars), feed_dict={z: z_train})
# print 'number of gradients computed: {}'.format(2*len(list_))
# print the current iteration
print('iteration={}/{}'.format(i + iterations_trained + 1, iterations))
# all 100 steps compute the losses and elapsed times, and generate images
if (i + iterations_trained) % 100 == 99:
# get time for last 100 iterations
elapsed_time = time.time() - t
# generate sample images from fixed noise
generate_image(i + iterations_trained + 1)
print 'generated images'
# compute and save losses on dev set
if architecture == 'DCGAN':
dev_d_loss = []
for images_dev, _ in dev_gen():
images_dev = images_dev.reshape([-1, 28, 28, 1])
z_train_dev = np.random.randn(batch_size, input_dim)
_dev_d_loss = session.run(d_loss,
feed_dict={
x_true: images_dev,
z: z_train_dev
})
dev_d_loss.append(_dev_d_loss)
tp_app = pd.DataFrame(data=[[
i + iterations_trained + 1, elapsed_time,
np.mean(dev_d_loss)
]],
index=None,
columns=['iteration', 'time', 'd_loss'])
training_progress = pd.concat([training_progress, tp_app],
axis=0,
ignore_index=True)
training_progress.to_csv(path_or_buf=directory +
'training_progress.csv')
else: # WGAN-GP
dev_W_dist = []
dev_d_loss = []
for images_dev, _ in dev_gen():
images_dev = images_dev.reshape([-1, 28, 28, 1])
z_train_dev = np.random.randn(batch_size, input_dim)
_dev_W_dist = session.run(wasserstein_dist,
feed_dict={
x_true: images_dev,
z: z_train_dev
})
_dev_d_loss = session.run(d_loss,
feed_dict={
x_true: images_dev,
z: z_train_dev
})
dev_W_dist.append(_dev_W_dist)
dev_d_loss.append(_dev_d_loss)
tp_app = pd.DataFrame(data=[[
i + iterations_trained + 1, elapsed_time,
np.mean(dev_W_dist),
np.mean(dev_d_loss)
]],
index=None,
columns=[
'iteration', 'time',
'Wasserstein_dist', 'd_loss'
])
training_progress = pd.concat([training_progress, tp_app],
axis=0,
ignore_index=True)
training_progress.to_csv(path_or_buf=directory +
'training_progress.csv')
print 'saved training progress'
print
# save model
saver.save(sess=session, save_path=model_dir + 'saved_model')
# save number of iterations trained
np.savetxt(fname=model_dir + 'iterations.csv',
X=[i + iterations_trained + 1])
print 'saved model after training iteration {}'.format(
i + iterations_trained + 1)
# fix new start time
t = time.time()
# average times:
if get_stats:
print '\n\naverage times over {} iterations:'.format(
iterations - iterations_trained)
print 'computation time to train for 1 iteration (minimize disc and gen one step): t1 = {}'.format(
np.mean(t1s))
print 'computation time to compute the disc. and gen. loss once: t2 = {}'.format(
np.mean(t2s))
print 'computation time to compute x_generated: t3 = {}'.format(
np.mean(t3s))
if architecture == 'WGANGP':
print 'computation time to compute gradient regularization term: t4 = {}'.format(
np.mean(t4s))
print 't1/t2 = {}'.format(np.mean(t1s) / np.mean(t2s))
print
# -------------------------------------------------------
# after training close the session
session.close()
tf.reset_default_graph()
# -------------------------------------------------------
# when training is done send email
if send:
subject = 'GAN (MNIST) partly fixed training finished'
body = 'to download the results of this model use (in the terminal):\n\n'
body += 'scp -r [email protected]:/cluster/home/fkrach/MasterThesis/MTCode1/' + directory + ' .'
files = [
directory + 'model_config.csv',
directory + 'training_progress.csv',
samples_dir + 'iteration_{}.png'.format(iterations)
]
send_email.send_email(subject=subject, body=body, file_names=files)
return directory
