def model_fn(features, labels, mode, params):
x = tf.reshape(features, [-1, 125, 161, 2], name='stricCnn5')
x_norm = tf.layers.batch_normalization(
x, training=mode == tf.estimator.ModeKeys.TRAIN, name='x_norm')
x = tf.reshape(x_norm[:, :, :, 0], [-1, 125, 161, 1], name='reshape_spec')
conv = x
conv = stric_block(conv, 32, mode, 'conv_1')
conv = stric_block(conv, 64, mode, 'conv_2')
conv = stric_block(conv, 128, mode, 'conv_3')
conv = stric_block(conv, 256, mode, 'conv_4')
conv = stric_block(conv, 512, mode, 'conv_5', only_same=True)
incep = inception_block(conv,
t1x1=128,
t3x3=128,
t5x5=128,
tmp=128,
name='incep6')
incep = inception_block(incep,
t1x1=128,
t3x3=128,
t5x5=128,
tmp=128,
name='incep7')
incep = inception_block(incep,
t1x1=128,
t3x3=128,
t5x5=128,
tmp=128,
name='incep8')
flat = flatten(incep)
dropout1 = tf.layers.dropout(flat,
rate=params['dropout_rate'],
training=mode == tf.estimator.ModeKeys.TRAIN,
name='dropout1')
dense1 = tf.layers.dense(dropout1,
units=2048,
activation=tf.nn.relu,
name='dense1')
dropout2 = tf.layers.dropout(dense1,
rate=params['dropout_rate'],
training=mode == tf.estimator.ModeKeys.TRAIN,
name='dropout2')
dense2 = tf.layers.dense(dropout2,
units=2048,
activation=tf.nn.relu,
name='dense2')
dropout3 = tf.layers.dropout(dense2,
rate=params['dropout_rate'],
training=mode == tf.estimator.ModeKeys.TRAIN,
name='dropout3')
logits = tf.layers.dense(dropout3,
units=params['num_classes'],
name='logits')
predictions = {
'classes': tf.argmax(logits, axis=1, name='prediction_classes'),
'probabilities': tf.nn.softmax(logits, name='prediction_softmax')
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(
mode=mode,
predictions={'predictions': predictions['probabilities']})
tf.summary.image(
'confusion_matrix',
conf_mat(labels, predictions['classes'], params['num_classes']))
onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32),
depth=params['num_classes'],
name='onehot_labels')
loss = tf.losses.softmax_cross_entropy(onehot_labels=onehot_labels,
logits=logits)
tf.summary.scalar('loss', loss)
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=params['learning_rate'])
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss=loss,
global_step=tf.train.get_global_step())
eval_metric_ops = {
'accuracy':
tf.metrics.accuracy(labels=labels, predictions=predictions['classes'])
}
tf.summary.scalar('accuracy', eval_metric_ops['accuracy'][1])
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops)
def model_fn(features, labels, mode, params):
x = tf.reshape(features, [-1, 125, 161, 2], name='cnn4')
x_norm = tf.layers.batch_normalization(
x, training=mode == tf.estimator.ModeKeys.TRAIN, name='x_norm')
x = tf.reshape(x_norm[:, :, :, 0], [-1, 125, 161, 1], name='reshape_spec')
if params['verbose_summary']:
tf.summary.image('input', x)
conv1 = tf.layers.conv2d(x,
filters=16,
kernel_size=5,
activation=tf.nn.relu,
name='conv1')
pool1 = tf.layers.max_pooling2d(conv1,
pool_size=[2, 2],
strides=2,
name='pool1')
if params['verbose_summary']:
log_conv_kernel('conv1')
tf.summary.image('pool1', pool1[:, :, :, 0:1])
conv2 = tf.layers.conv2d(pool1,
filters=32,
kernel_size=5,
activation=tf.nn.relu,
name='conv2')
pool2 = tf.layers.max_pooling2d(conv2,
pool_size=[2, 2],
strides=2,
name='pool2')
if params['verbose_summary']:
log_conv_kernel('conv2')
tf.summary.image('pool2', pool2[:, :, :, 0:1])
conv3 = tf.layers.conv2d(pool2,
filters=64,
kernel_size=5,
activation=tf.nn.relu,
name='conv3')
pool3 = tf.layers.max_pooling2d(conv3,
pool_size=[2, 2],
strides=2,
name='pool3')
if params['verbose_summary']:
log_conv_kernel('conv3')
tf.summary.image('pool3', pool3[:, :, :, 0:1])
dim = pool3.get_shape()[1:]
dim = int(dim[0] * dim[1] * dim[2])
flat = tf.reshape(pool3, [-1, dim], name='flat')
dropout4 = tf.layers.dropout(flat,
rate=params['dropout_rate'],
training=mode == ModeKeys.TRAIN,
name='dropout4')
dense4 = tf.layers.dense(dropout4,
units=128,
activation=tf.nn.relu,
name='dense4')
logits = tf.layers.dense(dense4,
units=params['num_classes'],
name='logits')
predictions = {
'classes': tf.argmax(logits, axis=1, name='prediction_classes'),
'probabilities': tf.nn.softmax(logits,
name='prediction_probabilities'),
}
if mode == ModeKeys.PREDICT:
return EstimatorSpec(
mode=mode,
predictions={'predictions': predictions['probabilities']})
tf.summary.image(
'confusion_matrix',
conf_mat(labels, predictions['classes'], params['num_classes']))
onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32),
depth=params['num_classes'],
name='onehot_labels')
loss = tf.losses.softmax_cross_entropy(onehot_labels=onehot_labels,
logits=logits)
tf.summary.scalar('loss', loss)
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=params['learning_rate'])
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss=loss,
global_step=tf.train.get_global_step())
eval_metric_ops = {
'accuracy':
tf.metrics.accuracy(labels=labels, predictions=predictions['classes'])
}
tf.summary.scalar('accuracy', eval_metric_ops['accuracy'][1])
return EstimatorSpec(mode=mode,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops)
def model_fn(features, labels, mode, params):
x = tf.reshape(features, [-1, 125, 161, 2], name='incep902')
x_norm = tf.layers.batch_normalization(x, training=mode == tf.estimator.ModeKeys.TRAIN, name='x_norm')
x = tf.reshape(x_norm[:, :, :, 0], [-1, 125, 161, 1], name='reshape_spec')
conv1 = tf.layers.conv2d(x, filters=16, kernel_size=3, padding='same', activation=tf.nn.relu, name='conv1')
conv1b = tf.layers.conv2d(conv1, filters=16, kernel_size=3, activation=tf.nn.relu, name='conv1b')
pool1 = tf.layers.max_pooling2d(conv1b, pool_size=[2, 2], strides=2, name='pool1')
if params['verbose_summary']:
log_conv_kernel('conv1')
log_conv_kernel('conv1b')
tf.summary.image('pool1', pool1[:, :, :, 0:1])
incep2 = inception_block(pool1, t1x1=8, t3x3=8, t5x5=8, tmp=8, name='incep2')
conv3 = tf.layers.conv2d(incep2, filters=32, kernel_size=3, padding='same', activation=tf.nn.relu, name='conv3')
conv3b = tf.layers.conv2d(conv3, filters=32, kernel_size=3, activation=tf.nn.relu, name='conv3b')
pool3 = tf.layers.max_pooling2d(conv3b, pool_size=[2, 2], strides=2, name='pool3')
if params['verbose_summary']:
log_conv_kernel('conv3')
log_conv_kernel('conv3b')
tf.summary.image('pool3', pool3[:, :, :, 0:1])
conv5 = tf.layers.conv2d(pool3, filters=64, kernel_size=3, padding='same', activation=tf.nn.relu, name='conv5')
conv5b = tf.layers.conv2d(conv5, filters=64, kernel_size=3, activation=tf.nn.relu, name='conv5b')
pool5 = tf.layers.max_pooling2d(conv5b, pool_size=[2, 2], strides=2, name='pool5')
if params['verbose_summary']:
log_conv_kernel('conv5')
log_conv_kernel('conv5b')
tf.summary.image('pool5', pool5[:, :, :, 0:1])
incep6 = inception_block(pool5, t1x1=32, t3x3=32, t5x5=32, tmp=32, name='incep6')
conv7 = tf.layers.conv2d(incep6, filters=128, kernel_size=3, padding='same', activation=tf.nn.relu, name='conv7')
conv7b = tf.layers.conv2d(conv7, filters=128, kernel_size=3, activation=tf.nn.relu, name='conv7b')
pool7 = tf.layers.max_pooling2d(conv7b, pool_size=[2, 2], strides=2, name='pool7')
if params['verbose_summary']:
log_conv_kernel('conv7')
log_conv_kernel('conv7b')
tf.summary.image('pool7', pool7[:, :, :, 0:1])
conv8 = tf.layers.conv2d(pool7, filters=256, kernel_size=3, padding='same', activation=tf.nn.relu, name='conv8')
conv8b = tf.layers.conv2d(conv8, filters=256, kernel_size=3, activation=tf.nn.relu, name='conv8b')
pool8 = tf.layers.max_pooling2d(conv8b, pool_size=[2, 2], strides=2, name='pool8')
if params['verbose_summary']:
log_conv_kernel('conv8')
log_conv_kernel('conv8b')
tf.summary.image('pool8', pool8[:, :, :, 0:1])
conv = tf.layers.conv2d(pool8, filters=256, kernel_size=[1, 3], activation=tf.nn.relu, name='conv1x1')
conv = tf.layers.conv2d(conv, filters=params['num_classes'], kernel_size=1, activation=None, name='conv_logits')
logits = tf.reshape(conv, [-1, 12], name='flatten')
predictions = {
'classes': tf.argmax(logits, axis=1, name='prediction_classes'),
'probabilities': tf.nn.softmax(logits, name='prediction_softmax')
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions={'predictions': predictions['probabilities']})
tf.summary.image('confusion_matrix', conf_mat(labels, predictions['classes'], params['num_classes']))
onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=params['num_classes'], name='onehot_labels')
loss = tf.losses.softmax_cross_entropy(onehot_labels=onehot_labels, logits=logits)
tf.summary.scalar('loss', loss)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=params['learning_rate'])
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())
eval_metric_ops = {
'accuracy': tf.metrics.accuracy(labels=labels, predictions=predictions['classes'])
}
tf.summary.scalar('accuracy', eval_metric_ops['accuracy'][1])
return tf.estimator.EstimatorSpec(
mode=mode,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops
)