diff --git a/python/create_savedmodel_maggiori17_fullyconv.py b/python/create_savedmodel_maggiori17_fullyconv.py index fa62bd466a8dd1467a0712346427ddc03e986cfb..32843e764c22249c36a8089af132e453e42114e9 100755 --- a/python/create_savedmodel_maggiori17_fullyconv.py +++ b/python/create_savedmodel_maggiori17_fullyconv.py @@ -3,7 +3,7 @@ #========================================================================== # # Copyright 2018-2019 Remi Cresson (IRSTEA) -# Copyright 2020 Remi Cresson (INRAE) +# Copyright 2020-2021 Remi Cresson (INRAE) # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -45,6 +45,8 @@ with tf.compat.v1.Graph().as_default(): # placeholder for images and labels lr = tf.compat.v1.placeholder_with_default(tf.constant(0.0002, dtype=tf.float32, shape=[]), shape=[], name="learning_rate") + training = tf.placeholder_with_default(tf.constant(False, dtype=tf.bool, shape=()), shape=(), + name="is_training") x = tf.compat.v1.placeholder(tf.float32, shape=(None, patch_size_xs, patch_size_xs, params.n_channels), name="x") y = tf.compat.v1.placeholder(tf.int32, shape=(None, patch_size_label, patch_size_label, 1), name="y") @@ -53,7 +55,7 @@ with tf.compat.v1.Graph().as_default(): activation=tf.nn.crelu) # Normalization of output of layer 1 - norm1 = tf.compat.v1.layers.batch_normalization(conv1) + norm1 = tf.compat.v1.layers.batch_normalization(conv1, training=training) # pooling layer #1 pool1 = tf.compat.v1.layers.max_pooling2d(inputs=norm1, pool_size=[4, 4], strides=4) @@ -63,14 +65,14 @@ with tf.compat.v1.Graph().as_default(): activation=tf.nn.crelu) # Normalization of output of layer 2 - norm2 = tf.compat.v1.layers.batch_normalization(conv2) + norm2 = tf.compat.v1.layers.batch_normalization(conv2, training=training) # Convolutional Layer #3 conv3 = tf.compat.v1.layers.conv2d(inputs=norm2, filters=80, kernel_size=[3, 3], padding="valid", activation=tf.nn.crelu) # Normalization of output of layer 3 - norm3 = tf.compat.v1.layers.batch_normalization(conv3) + norm3 = tf.compat.v1.layers.batch_normalization(conv3, training=training) # Convolutional Layer #4 conv4 = tf.compat.v1.layers.conv2d(inputs=norm3, filters=1, kernel_size=[8, 8], padding="valid",