Commit a8911582 authored by Oleg Dzhimiev's avatar Oleg Dzhimiev
Browse files

summary.histograms testing

parent 445d71c4

nn_ds_inmem4_tmp.py

+28 −7
Original line number Diff line number Diff line
@@ -30,7 +30,8 @@ FILES_PER_SCENE = 5 # number of random offset files for the scene to select f 
#MIN_BATCH_CHOICES = 10 # minimal number of tiles in a file for each bin to select from 

#MAX_BATCH_FILES =   10 #maximal number of files to use in a batch

MAX_EPOCH =        500

LR =               1e-4 # learning rate

#LR =               1e-4 # learning rate

LR =               1e-3 # learning rate

USE_CONFIDENCE =     False

ABSOLUTE_DISPARITY = False # True # False

DEBUG_PLT_LOSS =     True
@@ -183,7 +184,7 @@ dataset_train_size = len(corr2d_train) 
print_time("dataset_train.output_types "+str(dataset_train.output_types)+", dataset_train.output_shapes "+str(dataset_train.output_shapes)+", number of elements="+str(dataset_train_size))



dataset_train = dataset_train.batch(BATCH_SIZE)

#dataset_train = dataset_train.prefetch(BATCH_SIZE)

dataset_train = dataset_train.prefetch(BATCH_SIZE)



dataset_train_size //= BATCH_SIZE

print("dataset_train.output_types "+str(dataset_train.output_types)+", dataset_train.output_shapes "+str(dataset_train.output_shapes)+", number of elements="+str(dataset_train_size))
@@ -223,7 +224,13 @@ def network_fc_simple(input, arch = 0): 
                inp = fc[-1]

            else:

               inp = input

            

            fc.append(slim.fully_connected(inp, num_outs, activation_fn=lrelu,scope='g_fc'+str(i)))

            with tf.variable_scope('g_fc'+str(i)+'/fully_connected',reuse=tf.AUTO_REUSE):

                w = tf.get_variable('weights',shape=[inp.shape[1],num_outs])

                b = tf.get_variable('weights',shape=[inp.shape[1],num_outs])

                tf.summary.histogram("weights",w)

                tf.summary.histogram("biases",b)

    """

#  fc1  = slim.fully_connected(input, 256, activation_fn=lrelu,scope='g_fc1')

#  fc2  = slim.fully_connected(fc1,   128, activation_fn=lrelu,scope='g_fc2')
@@ -237,8 +244,21 @@ def network_fc_simple(input, arch = 0): 
  

    if USE_CONFIDENCE:

        fc_out  = slim.fully_connected(fc[-1],     2, activation_fn=lrelu,scope='g_fc_out')

        

        with tf.variable_scope('g_fc_out',reuse=tf.AUTO_REUSE):

            w = tf.get_variable('weights',shape=[fc[-1].shape[1],2])

            b = tf.get_variable('biases',shape=[fc[-1].shape[1],2])

            tf.summary.histogram("weights",w)

            tf.summary.histogram("biases",b)

            

    else:     

        fc_out  = slim.fully_connected(fc[-1],     1, activation_fn=None,scope='g_fc_out')

        

        with tf.variable_scope('g_fc_out',reuse=tf.AUTO_REUSE):

            w = tf.get_variable('weights',shape=[fc[-1].shape[1],1])

            b = tf.get_variable('biases',shape=[1])

            tf.summary.histogram("weights",w)

            tf.summary.histogram("biases",b)

        #If using residual disparity, split last layer into 2 or remove activation and add rectifier to confidence only  

    return fc_out
@@ -399,7 +419,8 @@ with tf.name_scope('epoch_average'): 


t_vars=tf.trainable_variables()

lr=tf.placeholder(tf.float32)

G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(G_loss)

#G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(G_loss)

G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(_cost1)



saver=tf.train.Saver()