Commit 0f89fcdb authored by Amir MOHAMMADI's avatar Amir MOHAMMADI
Browse files

Account for when regularization is not used [skip ci]

parent 63176ee5
Pipeline #19503 skipped
......@@ -223,7 +223,8 @@ def architecture(input_layer,
regularizer=regularizer):
bn7_act, endpoints = base_architecture(
input_layer, mode, data_format, skip_first_two_pool)
input_layer=input_layer, mode=mode, data_format=data_format,
skip_first_two_pool=skip_first_two_pool)
# Logits layer
logits = tf.layers.dense(inputs=bn7_act, units=n_classes)
endpoints['FC-3'] = logits
......@@ -238,6 +239,8 @@ def model_fn(features, labels, mode, params=None, config=None):
key = features['key']
params = params or {}
params = {k: v for k, v in params.items() if v is not None}
initial_learning_rate = params.get('learning_rate', 1e-3)
momentum = params.get('momentum', 0.99)
decay_steps = params.get('decay_steps', 1e5)
......@@ -253,7 +256,7 @@ def model_fn(features, labels, mode, params=None, config=None):
}
arch_kwargs = {k: v for k, v in arch_kwargs.items() if v is not None}
logits, _ = architecture(data, mode, **arch_kwargs)
logits, _ = architecture(data, mode=mode, **arch_kwargs)
predictions = {
# Generate predictions (for PREDICT and EVAL mode)
......@@ -269,8 +272,9 @@ def model_fn(features, labels, mode, params=None, config=None):
# Calculate Loss (for both TRAIN and EVAL modes)
loss = tf.losses.sparse_softmax_cross_entropy(logits=logits, labels=labels)
# Add the regularization terms to the loss
loss += regularization_rate * \
tf.add_n(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
if tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES):
loss += regularization_rate * \
tf.add_n(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
accuracy = tf.metrics.accuracy(
labels=labels, predictions=predictions["classes"])
......
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