def testMaxPooling3DGradient(self):
def forward(a):
r = max_pooling3d(a,
pool_size=pool_size,
strides=strides,
padding='SAME')
return r
input_sizes = [1, 3, 2, 4, 1]
pool_size = (2, 2, 1)
strides = (1, 1, 1)
total_size = np.prod(input_sizes)
x = np.arange(1, total_size + 1, dtype=np.float32)
aa = constant_op.constant(x, shape=input_sizes, dtype=dtypes.float32)
da = backprop.gradients_function(forward)(aa)
if not context.executing_eagerly():
tf_aa = constant_op.constant(x,
shape=input_sizes,
dtype=dtypes.float32)
tf_max = max_pooling3d(tf_aa,
pool_size=pool_size,
strides=strides,
padding='SAME')
tf_da = gradients.gradients(tf_max, [tf_aa])
self.assertAllEqual(da[0], tf_da[0].eval())
def testMaxPooling3DGradient(self):
def forward(a):
r = max_pooling3d(a, pool_size=pool_size, strides=strides, padding='SAME')
return r
input_sizes = [1, 3, 2, 4, 1]
pool_size = (2, 2, 1)
strides = (1, 1, 1)
total_size = np.prod(input_sizes)
x = np.arange(1, total_size + 1, dtype=np.float32)
aa = constant_op.constant(x, shape=input_sizes, dtype=dtypes.float32)
da = backprop.gradients_function(forward)(aa)
if not context.executing_eagerly():
tf_aa = constant_op.constant(x, shape=input_sizes, dtype=dtypes.float32)
tf_max = max_pooling3d(
tf_aa, pool_size=pool_size, strides=strides, padding='SAME')
tf_da = gradients.gradients(tf_max, [tf_aa])
self.assertAllEqual(da[0], tf_da[0].eval())
def cnn_3d(images, is_training):
"""
Build the model for 2D-CNN.
Inputs:
-- images: Images placeholder
-- is_training: bool placeholder, training or not
Output:
-- Logits: Return the output of the model
"""
# Size of images & labels
height = int(images.shape[1])
width = int(images.shape[2])
depth = int(images.shape[3])
images = tf.reshape(images, [-1, height, width, depth, 1])
# Build the model
with tf.name_scope('CONV1'):
l_conv1 = conv3d(images,
filters=4,
kernel_size=[3, 3, 10],
strides=[1, 1, 5],
activation=relu,
kernel_regularizer=l2_regularizer(REG_lambda))
# l_conv1 = rrelu(l_conv1, is_training)
l_maxpool1 = max_pooling3d(l_conv1,
pool_size=[3, 3, 3],
strides=[1, 1, 1],
padding='same',
name='Maxpool1')
with tf.name_scope('CONV2'):
l_conv2 = conv3d(
l_maxpool1,
filters=16,
kernel_size=[3, 3, 10],
strides=[1, 1, 2],
# activation=relu,
kernel_regularizer=l2_regularizer(REG_lambda))
l_conv2 = rrelu(l_conv2, is_training)
l_maxpool2 = max_pooling3d(l_conv2,
pool_size=[3, 3, 3],
strides=[1, 1, 1],
padding='same',
name='Maxpool2')
l_flatten = flatten(l_maxpool2, scope='Flatten')
with tf.name_scope('FC1'):
l_fc1 = dense(
l_flatten,
200,
kernel_regularizer=l2_regularizer(REG_lambda),
# activation=relu
)
l_fc1 = rrelu(l_fc1, is_training)
l_drop1 = dropout(l_fc1,
Utils.drop,
training=is_training,
name='Dropout1')
with tf.name_scope('FC2'):
l_fc2 = dense(
l_drop1,
200,
kernel_regularizer=l2_regularizer(REG_lambda),
# activation=relu
)
l_fc2 = rrelu(l_fc2, is_training)
l_drop2 = dropout(l_fc2,
Utils.drop,
training=is_training,
name='Dropout2')
logits = dense(l_drop2, NUM_CLASSES, name='Output')
return logits
def forward(a): r = max_pooling3d(a, pool_size=pool_size, strides=strides, padding='SAME') return r
def forward(a):
r = max_pooling3d(a,
pool_size=pool_size,
strides=strides,
padding='SAME')
return r
