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Commit 57851864 authored by Amir MOHAMMADI's avatar Amir MOHAMMADI
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Complete patchCNN

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bob/learn/tensorflow/network/PatchCNN.py
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......@@ -47,7 +47,7 @@ import tensorflow as tf
def base_architecture(input_layer, mode, data_format, **kwargs):
# Keep track of all the endpoints
endpoints = {}
bn_axis = 1 if data_format.lower() == 'channels_first' else -1
bn_axis = 1 if data_format.lower() == 'channels_first' else 3
training = mode == tf.estimator.ModeKeys.TRAIN
# ======================
......@@ -57,17 +57,20 @@ def base_architecture(input_layer, mode, data_format, **kwargs):
filters=50,
kernel_size=(5, 5),
padding="same",
activation=tf.nn.relu,
activation=None,
data_format=data_format)
endpoints['Conv-1'] = conv1
# Batch Normalization #1
bn1 = tf.layers.batch_normalization(conv1, axis=bn_axis, training=training)
bn1 = tf.layers.batch_normalization(
conv1, axis=bn_axis, training=training, fused=True)
endpoints['BN-1'] = bn1
bn1_act = tf.nn.relu(bn1)
endpoints['BN-1-activation'] = bn1_act
# Pooling Layer #1
pool1 = tf.layers.max_pooling2d(
inputs=bn1, pool_size=[2, 2], strides=2, data_format=data_format)
inputs=bn1_act, pool_size=[2, 2], strides=2, data_format=data_format)
endpoints['MaxPooling-1'] = pool1
# ======================
......@@ -77,17 +80,20 @@ def base_architecture(input_layer, mode, data_format, **kwargs):
filters=100,
kernel_size=(3, 3),
padding="same",
activation=tf.nn.relu,
activation=None,
data_format=data_format)
endpoints['Conv-2'] = conv2
# Batch Normalization #2
bn2 = tf.layers.batch_normalization(conv2, axis=bn_axis, training=training)
bn2 = tf.layers.batch_normalization(
conv2, axis=bn_axis, training=training, fused=True)
endpoints['BN-2'] = bn2
bn2_act = tf.nn.relu(bn2)
endpoints['BN-2-activation'] = bn2_act
# Pooling Layer #2
pool2 = tf.layers.max_pooling2d(
inputs=bn2, pool_size=[2, 2], strides=2, data_format=data_format)
inputs=bn2_act, pool_size=[2, 2], strides=2, data_format=data_format)
endpoints['MaxPooling-2'] = pool2
# ======================
......@@ -97,17 +103,20 @@ def base_architecture(input_layer, mode, data_format, **kwargs):
filters=150,
kernel_size=(3, 3),
padding="same",
activation=tf.nn.relu,
activation=None,
data_format=data_format)
endpoints['Conv-3'] = conv3
# Batch Normalization #3
bn3 = tf.layers.batch_normalization(conv3, axis=bn_axis, training=training)
bn3 = tf.layers.batch_normalization(
conv3, axis=bn_axis, training=training, fused=True)
endpoints['BN-3'] = bn3
bn3_act = tf.nn.relu(bn3)
endpoints['BN-3-activation'] = bn3_act
# Pooling Layer #3
pool3 = tf.layers.max_pooling2d(
inputs=bn3, pool_size=[3, 3], strides=2, data_format=data_format)
inputs=bn3_act, pool_size=[3, 3], strides=2, data_format=data_format)
endpoints['MaxPooling-3'] = pool3
# ======================
......@@ -117,17 +126,20 @@ def base_architecture(input_layer, mode, data_format, **kwargs):
filters=200,
kernel_size=(3, 3),
padding="same",
activation=tf.nn.relu,
activation=None,
data_format=data_format)
endpoints['Conv-4'] = conv4
# Batch Normalization #4
bn4 = tf.layers.batch_normalization(conv4, axis=bn_axis, training=training)
bn4 = tf.layers.batch_normalization(
conv4, axis=bn_axis, training=training, fused=True)
endpoints['BN-4'] = bn4
bn4_act = tf.nn.relu(bn4)
endpoints['BN-4-activation'] = bn4_act
# Pooling Layer #4
pool4 = tf.layers.max_pooling2d(
inputs=bn4, pool_size=[2, 2], strides=2, data_format=data_format)
inputs=bn4_act, pool_size=[2, 2], strides=2, data_format=data_format)
endpoints['MaxPooling-4'] = pool4
# ======================
......@@ -137,17 +149,20 @@ def base_architecture(input_layer, mode, data_format, **kwargs):
filters=250,
kernel_size=(3, 3),
padding="same",
activation=tf.nn.relu,
activation=None,
data_format=data_format)
endpoints['Conv-5'] = conv5
# Batch Normalization #5
bn5 = tf.layers.batch_normalization(conv5, axis=bn_axis, training=training)
bn5 = tf.layers.batch_normalization(
conv5, axis=bn_axis, training=training, fused=True)
endpoints['BN-5'] = bn5
bn5_act = tf.nn.relu(bn5)
endpoints['BN-5-activation'] = bn5_act
# Pooling Layer #5
pool5 = tf.layers.max_pooling2d(
inputs=bn5, pool_size=[2, 2], strides=2, data_format=data_format)
inputs=bn5_act, pool_size=[2, 2], strides=2, data_format=data_format)
endpoints['MaxPooling-5'] = pool5
# Flatten tensor into a batch of vectors
......@@ -157,27 +172,33 @@ def base_architecture(input_layer, mode, data_format, **kwargs):
# ========================
# Fully Connected Layer #1
fc_1 = tf.layers.dense(
inputs=pool5_flat, units=1000, activation=tf.nn.relu)
inputs=pool5_flat, units=1000, activation=None)
endpoints['FC-1'] = fc_1
# Batch Normalization #6
bn6 = tf.layers.batch_normalization(fc_1, axis=bn_axis, training=training)
bn6 = tf.layers.batch_normalization(
fc_1, axis=bn_axis, training=training, fused=True)
endpoints['BN-6'] = bn6
bn6_act = tf.nn.relu(bn6)
endpoints['BN-6-activation'] = bn6_act
# Dropout
dropout = tf.layers.dropout(inputs=bn6, rate=0.5, training=training)
dropout = tf.layers.dropout(inputs=bn6_act, rate=0.5, training=training)
endpoints['dropout'] = dropout
# ========================
# Fully Connected Layer #2
fc_2 = tf.layers.dense(inputs=dropout, units=400, activation=tf.nn.relu)
fc_2 = tf.layers.dense(inputs=dropout, units=400, activation=None)
endpoints['FC-2'] = fc_2
# Batch Normalization #7
bn7 = tf.layers.batch_normalization(fc_2, axis=bn_axis, training=training)
bn7 = tf.layers.batch_normalization(
fc_2, axis=bn_axis, training=training, fused=True)
endpoints['BN-7'] = bn7
bn7_act = tf.nn.relu(bn7)
endpoints['BN-7-activation'] = bn7_act
return bn7, endpoints
return bn7_act, endpoints
def architecture(input_layer,
......@@ -189,9 +210,9 @@ def architecture(input_layer,
with tf.variable_scope('PatchCNN', reuse=reuse):
bn7, endpoints = base_architecture(input_layer, mode, data_format)
bn7_act, endpoints = base_architecture(input_layer, mode, data_format)
# Logits layer
logits = tf.layers.dense(inputs=bn7, units=n_classes)
logits = tf.layers.dense(inputs=bn7_act, units=n_classes)
endpoints['FC-3'] = logits
endpoints['logits'] = logits
......@@ -204,7 +225,8 @@ def model_fn(features, labels, mode, params=None, config=None):
key = features['key']
params = params or {}
learning_rate = params.get('learning_rate', 1e-3)
initial_learning_rate = params.get('learning_rate', 1e-3)
momentum = params.get('momentum', 0.99)
arch_kwargs = {
'n_classes': params.get('n_classes', None),
......@@ -233,14 +255,24 @@ def model_fn(features, labels, mode, params=None, config=None):
# Configure the training op
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=learning_rate)
learning_rate = tf.train.exponential_decay(
learning_rate=initial_learning_rate,
global_step=tf.train.get_or_create_global_step(),
decay_steps=1e5,
decay_rate=1e-4)
optimizer = tf.train.MomentumOptimizer(
learning_rate=learning_rate,
momentum=momentum)
train_op = optimizer.minimize(
loss=loss, global_step=tf.train.get_or_create_global_step())
# Log accuracy and loss
with tf.name_scope('train_metrics'):
tf.summary.scalar('accuracy', accuracy[1])
tf.summary.scalar('loss', loss)
tf.summary.scalar('learning_rate', learning_rate)
else:
train_op = None
......
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