def bulid_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 = mo.Model(input_size,
layerSize,
num_of_classes,
learning_rate_local,
save_file,
int(activation_function),
cov_net=covn_net)
return model
def estimate_IY_by_network(data, labels):
tf.reset_default_graph()
input_size = data.shape[1]
# For each epoch and for each layer we calculate the best decoder - we train a 2 lyaer network
model = mo.Model(input_size, [400, 100, 50],
labels.shape[1],
0.001,
'',
cov_net=0)
optimizer = model.optimize
init = tf.global_variables_initializer()
num_of_ephocs = 60
batch_size = 1024
batch_points = np.rint(np.arange(0, data.shape[0] + 1,
batch_size)).astype(dtype=np.int32)
PYs = np.sum(labels, axis=0) / labels.shape[0]
Hy = np.nansum(-PYs * np.log2(PYs + np.spacing(1)))
print('rrrrr')
with tf.Session() as sess:
sess.run(init)
# Go over the epochs
for j in range(0, num_of_ephocs):
for i in xrange(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}
optimizer.run(feed_dict)
#if np.mod(j, 1000) == 1:
feed_dict = {model.x: data, model.labels: labels}
print(j, sess.run(model.accuracy, feed_dict=feed_dict))
# for i in range(data.shape[0]):
feed_dict = {model.x: np.matrix(data)}
p_y_given_t_i = sess.run(model.prediction, feed_dict=feed_dict)
true_label_index = np.argmax(labels, 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)])
sess.close()
print('jjj')
I_TY = Hy + I_TY
I_TY = I_TY if I_TY >= 0 else 0
return I_TY