python.mlsql_tf.py Maven / Gradle / Ivy
The newest version!
import tensorflow as tf
def fc_layer(input, size_in, size_out, active="relu", name="fc"):
with tf.name_scope(name):
w = tf.Variable(tf.truncated_normal([size_in, size_out], stddev=0.1), name="W_" + name)
b = tf.Variable(tf.constant(0.1, shape=[size_out], name="B_" + name))
if active == "sigmoid":
act = tf.nn.sigmoid(tf.matmul(input, w) + b)
elif active is None:
act = tf.matmul(input, w) + b
else:
act = tf.nn.relu(tf.matmul(input, w) + b)
tf.summary.histogram("W_" + name + "_weights", w)
tf.summary.histogram("B_" + name + "_biases", b)
tf.summary.histogram(name + "_activations", act)
return act
def conv_poo_layer(input, size_in, size_out, filter_width, filter_height, include_pool=False, name="conv"):
with tf.name_scope(name):
w = tf.Variable(tf.truncated_normal([filter_height, filter_width, size_in, size_out], stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[size_out], name="B"))
conv = tf.nn.conv2d(input, w, strides=[1, 1, 1, 1], padding="VALID")
act = tf.nn.relu(conv + b)
tf.summary.histogram("weights", w)
tf.summary.histogram("biases", b)
tf.summary.histogram("activations", act)
if include_pool:
return tf.nn.max_pool(act, ksize=[1, filter_height, 1, 1], strides=[1, 1, 1, 1], padding="VALID")
else:
return act