def test_vbs1(self):
with tf.Graph().as_default():
# Data loading and preprocessing
import zqtflearn.datasets.mnist as mnist
X, Y, testX, testY = mnist.load_data(one_hot=True)
X = X.reshape([-1, 28, 28, 1])
testX = testX.reshape([-1, 28, 28, 1])
X = X[:20, :, :, :]
Y = Y[:20, :]
testX = testX[:10, :, :, :]
testY = testY[:10, :]
# Building convolutional network
network = input_data(shape=[None, 28, 28, 1], name='input')
network = conv_2d(network,
32,
3,
activation='relu',
regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network,
64,
3,
activation='relu',
regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = fully_connected(network, 128, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 10, activation='softmax')
network = regression(network,
optimizer='adam',
learning_rate=0.01,
loss='categorical_crossentropy',
name='target')
# Training
model = zqtflearn.DNN(network, tensorboard_verbose=3)
model.fit({'input': X}, {'target': Y},
n_epoch=1,
batch_size=10,
validation_set=({
'input': testX
}, {
'target': testY
}),
validation_batch_size=5,
snapshot_step=10,
show_metric=True,
run_id='convnet_mnist_vbs')
self.assertEqual(model.train_ops[0].validation_batch_size, 5)
self.assertEqual(model.train_ops[0].batch_size, 10)
def make_core_network(network):
network = zqtflearn.reshape(network, [-1, 28, 28, 1], name="reshape")
network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network, 64, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = fully_connected(network, 128, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 10, activation='softmax')
return network
def test_vm1(self):
with tf.Graph().as_default():
# Data loading and preprocessing
import zqtflearn.datasets.mnist as mnist
X, Y, testX, testY = mnist.load_data(one_hot=True)
X = X.reshape([-1, 28, 28, 1])
testX = testX.reshape([-1, 28, 28, 1])
X = X[:10, :, :, :]
Y = Y[:10, :]
# Building convolutional network
network = input_data(shape=[None, 28, 28, 1], name='input')
network = conv_2d(network,
32,
3,
activation='relu',
regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network,
64,
3,
activation='relu',
regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = fully_connected(network, 128, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, 0.8)
# construct two varaibles to add as additional "valiation monitors"
# these varaibles are evaluated each time validation happens (eg at a snapshot)
# and the results are summarized and output to the tensorboard events file,
# together with the accuracy and loss plots.
#
# Here, we generate a dummy variable given by the sum over the current
# network tensor, and a constant variable. In practice, the validation
# monitor may present useful information, like confusion matrix
# entries, or an AUC metric.
with tf.name_scope('CustomMonitor'):
test_var = tf.reduce_sum(tf.cast(network, tf.float32),
name="test_var")
test_const = tf.constant(32.0, name="custom_constant")
print("network=%s, test_var=%s" % (network, test_var))
network = fully_connected(network, 10, activation='softmax')
network = regression(network,
optimizer='adam',
learning_rate=0.01,
loss='categorical_crossentropy',
name='target',
validation_monitors=[test_var, test_const])
# Training
model = zqtflearn.DNN(network, tensorboard_verbose=3)
model.fit({'input': X}, {'target': Y},
n_epoch=1,
validation_set=({
'input': testX
}, {
'target': testY
}),
snapshot_step=10,
show_metric=True,
run_id='convnet_mnist')
# check for validation monitor variables
ats = tf.get_collection("Adam_testing_summaries")
print("ats=%s" % ats)
self.assertTrue(
len(ats) == 4
) # should be four variables being summarized: [loss, test_var, test_const, accuracy]
session = model.session
print("session=%s" % session)
trainer = model.trainer
print("train_ops = %s" % trainer.train_ops)
top = trainer.train_ops[0]
vmtset = top.validation_monitors_T
print("validation_monitors_T = %s" % vmtset)
with model.session.as_default():
ats_var_val = zqtflearn.variables.get_value(vmtset[0])
ats_const_val = zqtflearn.variables.get_value(vmtset[1])
print("summary values: var=%s, const=%s" %
(ats_var_val, ats_const_val))
self.assertTrue(
ats_const_val ==
32) # test to make sure the constant made it through
3,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_2a_3x3')))
conv2b_3_3 = relu(
batch_normalization(
conv_2d(conv2a_3_3,
64,
3,
bias=False,
activation=None,
name='Conv2d_2b_3x3')))
maxpool3a_3_3 = max_pool_2d(conv2b_3_3,
3,
strides=2,
padding='VALID',
name='MaxPool_3a_3x3')
conv3b_1_1 = relu(
batch_normalization(
conv_2d(maxpool3a_3_3,
80,
1,
bias=False,
padding='VALID',
activation=None,
name='Conv2d_3b_1x1')))
conv4a_3_3 = relu(
batch_normalization(
conv_2d(conv3b_1_1,
192,
import zqtflearn from zqtflearn.layers.core import input_data, dropout, fully_connected from zqtflearn.layers.conv import conv_2d, max_pool_2d from zqtflearn.layers.estimator import regression # Data loading and preprocessing import zqtflearn.datasets.oxflower17 as oxflower17 X, Y = oxflower17.load_data(one_hot=True) # Building 'VGG Network' network = input_data(shape=[None, 224, 224, 3]) network = conv_2d(network, 64, 3, activation='relu') network = conv_2d(network, 64, 3, activation='relu') network = max_pool_2d(network, 2, strides=2) network = conv_2d(network, 128, 3, activation='relu') network = conv_2d(network, 128, 3, activation='relu') network = max_pool_2d(network, 2, strides=2) network = conv_2d(network, 256, 3, activation='relu') network = conv_2d(network, 256, 3, activation='relu') network = conv_2d(network, 256, 3, activation='relu') network = max_pool_2d(network, 2, strides=2) network = conv_2d(network, 512, 3, activation='relu') network = conv_2d(network, 512, 3, activation='relu') network = conv_2d(network, 512, 3, activation='relu') network = max_pool_2d(network, 2, strides=2)
from zqtflearn.layers.normalization import local_response_normalization, batch_normalization
from zqtflearn.layers.estimator import regression
# Data loading and preprocessing
import zqtflearn.datasets.mnist as mnist
X, Y, testX, testY = mnist.load_data(one_hot=True)
X = X.reshape([-1, 28, 28, 1])
testX = testX.reshape([-1, 28, 28, 1])
# Building convolutional network
network = input_data(shape=[None, 28, 28, 1], name='input')
#highway convolutions with pooling and dropout
for i in range(3):
for j in [3, 2, 1]:
network = highway_conv_2d(network, 16, j, activation='elu')
network = max_pool_2d(network, 2)
network = batch_normalization(network)
network = fully_connected(network, 128, activation='elu')
network = fully_connected(network, 256, activation='elu')
network = fully_connected(network, 10, activation='softmax')
network = regression(network,
optimizer='adam',
learning_rate=0.01,
loss='categorical_crossentropy',
name='target')
# Training
model = zqtflearn.DNN(network, tensorboard_verbose=0)
model.fit(X,
Y,
from zqtflearn.layers.estimator import regression
# Building 'VGG Network'
input_layer = input_data(shape=[None, 224, 224, 3])
block1_conv1 = conv_2d(input_layer,
64,
3,
activation='relu',
name='block1_conv1')
block1_conv2 = conv_2d(block1_conv1,
64,
3,
activation='relu',
name='block1_conv2')
block1_pool = max_pool_2d(block1_conv2, 2, strides=2, name='block1_pool')
block2_conv1 = conv_2d(block1_pool,
128,
3,
activation='relu',
name='block2_conv1')
block2_conv2 = conv_2d(block2_conv1,
128,
3,
activation='relu',
name='block2_conv2')
block2_pool = max_pool_2d(block2_conv2, 2, strides=2, name='block2_pool')
block3_conv1 = conv_2d(block2_pool,
256,
from zqtflearn.layers.conv import conv_2d, max_pool_2d, avg_pool_2d
from zqtflearn.layers.estimator import regression
# Data loading and preprocessing
from zqtflearn.datasets import cifar10
(X, Y), (X_test, Y_test) = cifar10.load_data()
X, Y = shuffle(X, Y)
Y = to_categorical(Y)
Y_test = to_categorical(Y_test)
# Building 'Network In Network'
network = input_data(shape=[None, 32, 32, 3])
network = conv_2d(network, 192, 5, activation='relu')
network = conv_2d(network, 160, 1, activation='relu')
network = conv_2d(network, 96, 1, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = dropout(network, 0.5)
network = conv_2d(network, 192, 5, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = avg_pool_2d(network, 3, strides=2)
network = dropout(network, 0.5)
network = conv_2d(network, 192, 3, activation='relu')
network = conv_2d(network, 192, 1, activation='relu')
network = conv_2d(network, 10, 1, activation='relu')
network = avg_pool_2d(network, 8)
network = flatten(network)
network = regression(network,
optimizer='adam',
loss='softmax_categorical_crossentropy',
learning_rate=0.001)
from zqtflearn.layers.conv import conv_2d, max_pool_2d, avg_pool_2d
from zqtflearn.layers.normalization import local_response_normalization
from zqtflearn.layers.merge_ops import merge
from zqtflearn.layers.estimator import regression
import zqtflearn.datasets.oxflower17 as oxflower17
X, Y = oxflower17.load_data(one_hot=True, resize_pics=(227, 227))
network = input_data(shape=[None, 227, 227, 3])
conv1_7_7 = conv_2d(network,
64,
7,
strides=2,
activation='relu',
name='conv1_7_7_s2')
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2)
pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_2d(pool1_3_3,
64,
1,
activation='relu',
name='conv2_3_3_reduce')
conv2_3_3 = conv_2d(conv2_3_3_reduce,
192,
3,
activation='relu',
name='conv2_3_3')
conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_2d(conv2_3_3,
kernel_size=3,
strides=2,