def _add_triaining_graph():
with tf.Graph().as_default() as graph:
logits = define_audio_slim(training=True)
tf.summary.histogram('logits', logits)
# define training subgraph
with tf.variable_scope('train'):
labels = tf.placeholder(tf.float32,
shape=[None, params.NUM_CLASSES],
name='labels')
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(
logits=logits, labels=labels, name='cross_entropy')
loss = tf.reduce_mean(cross_entropy, name='loss_op')
tf.summary.scalar('loss', loss)
# training
global_step = tf.Variable(0,
name='global_step',
trainable=False,
collections=[
tf.GraphKeys.GLOBAL_VARIABLES,
tf.GraphKeys.GLOBAL_STEP
])
optimizer = tf.train.AdamOptimizer(
learning_rate=params.LEARNING_RATE,
epsilon=params.ADAM_EPSILON)
optimizer.minimize(loss, global_step=global_step, name='train_op')
return graph
def _restore_from_defined_and_ckpt(sess, ckpt):
"""Restore graph from define and variables from ckpt file."""
with sess.graph.as_default():
audio_model.define_audio_slim(training=False)
audio_model.load_audio_slim_checkpoint(sess, ckpt)
Print to stdout an analysis of the number of floating point operations in the
model broken down by individual operations.
"""
tf.profiler.profile(
graph=graph,
options=tf.profiler.ProfileOptionBuilder.float_operation(),
cmd='scope')
if __name__ == '__main__':
X, y = np.arange(20).reshape((10, 2)), np.arange(10)
print(X)
print(y)
tr, te, vl = train_test_val_split(X, y, shuffle=True)
print(tr)
print(te)
print(vl)
import sys
sys.path.append('..')
sys.path.append('../vggish')
from audio_model import define_audio_slim
from vggish_slim import define_vggish_slim
import tensorflow as tf
with tf.Graph().as_default() as graph:
# define_vggish_slim(training=False)
define_audio_slim(training=False)
calculate_flops(graph)
pass