def build_model(activation_function, layerSize, input_size, num_of_classes,
learning_rate_local, save_file, covn_net):
"""Bulid specipic model of the network
Return the network model
"""
model = mo.Model(input_size,
layerSize,
num_of_classes,
learning_rate_local,
save_file,
int(activation_function),
cov_net=covn_net)
return model
def build_model(*args):
return mo.Model(*args)
def estimate_IY_by_network(data, labels, from_layer=0):
if len(data.shape) > 2:
input_size = data.shape[1:]
else:
input_size = data.shape[1]
p_y_given_t_i = data
acc_all = [0]
if from_layer < 5:
acc_all = []
g1 = tf.Graph() ## This is one graph
with g1.as_default():
# For each epoch and for each layer we calculate the best decoder - we train a 2 lyaer network
cov_net = 4
model = mo.Model(input_size, [400, 100, 50],
labels.shape[1],
0.0001,
'',
cov_net=cov_net,
from_layer=from_layer)
if from_layer < 5:
optimizer = model.optimize
init = tf.global_variables_initializer()
num_of_ephocs = 50
batch_size = 51
batch_points = np.rint(np.arange(
0, data.shape[0] + 1, batch_size)).astype(dtype=np.int32)
if data.shape[0] not in batch_points:
batch_points = np.append(batch_points, [data.shape[0]])
with tf.Session(graph=g1) as sess:
sess.run(init)
if from_layer < 5:
for j in range(0, num_of_ephocs):
for i in range(0, len(batch_points) - 1):
batch_xs = data[batch_points[i]:batch_points[i + 1], :]
batch_ys = labels[batch_points[i]:batch_points[i +
1], :]
feed_dict = {model.x: batch_xs, model.labels: batch_ys}
if cov_net == 1:
feed_dict[model.drouput] = 0.5
optimizer.run(feed_dict)
p_y_given_t_i = []
batch_size = 256
batch_points = np.rint(np.arange(
0, data.shape[0] + 1, batch_size)).astype(dtype=np.int32)
if data.shape[0] not in batch_points:
batch_points = np.append(batch_points, [data.shape[0]])
for i in range(0, len(batch_points) - 1):
batch_xs = data[batch_points[i]:batch_points[i + 1], :]
batch_ys = labels[batch_points[i]:batch_points[i + 1], :]
feed_dict = {model.x: batch_xs, model.labels: batch_ys}
if cov_net == 1:
feed_dict[model.drouput] = 1
p_y_given_t_i_local, acc = sess.run(
[model.prediction, model.accuracy], feed_dict=feed_dict)
acc_all.append(acc)
if i == 0:
p_y_given_t_i = np.array(p_y_given_t_i_local)
else:
p_y_given_t_i = np.concatenate(
(p_y_given_t_i, np.array(p_y_given_t_i_local)), axis=0)
# print ("The accuracy of layer number - {} - {}".format(from_layer, np.mean(acc_all)))
max_indx = len(p_y_given_t_i)
labels_cut = labels[:max_indx, :]
true_label_index = np.argmax(labels_cut, 1)
s = np.log2(p_y_given_t_i[np.arange(len(p_y_given_t_i)), true_label_index])
I_TY = np.mean(s[np.isfinite(s)])
PYs = np.sum(labels_cut, axis=0) / labels_cut.shape[0]
Hy = np.nansum(-PYs * np.log2(PYs + np.spacing(1)))
I_TY = Hy + I_TY
I_TY = I_TY if I_TY >= 0 else 0
acc = np.mean(acc_all)
sys.stdout.flush()
return I_TY, acc