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Update sum_of_squares (TF 0.10) to mean_squared_error (TF 0.12) #783

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Merged
merged 3 commits into from
Mar 16, 2017
Merged

Update sum_of_squares (TF 0.10) to mean_squared_error (TF 0.12) #783

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16da002
Update deprecated sum_of_squares (TF 0.10) to mean_squared_error (TF …
dweekly Dec 21, 2016
d50d211
Fix deprecated call to tf.image_summary to tf.summary.image
dweekly Dec 28, 2016
48f5b4d
Update to use tf.losses.mean_squared_error
nealwu Mar 16, 2017
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2 changes: 1 addition & 1 deletion im2txt/im2txt/ops/image_processing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -92,7 +92,7 @@ def process_image(encoded_image,
# only logged in thread 0.
def image_summary(name, image):
if not thread_id:
tf.image_summary(name, tf.expand_dims(image, 0))
tf.summary.image(name, tf.expand_dims(image, 0))

# Decode image into a float32 Tensor of shape [?, ?, 3] with values in [0, 1).
with tf.name_scope("decode", values=[encoded_image]):
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8 changes: 4 additions & 4 deletions slim/slim_walkthough.ipynb
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Expand Up @@ -232,7 +232,7 @@
},
"outputs": [],
"source": [
"# The following snippet trains the regression model using a sum_of_squares loss.\n",
"# The following snippet trains the regression model using a mean_squared_error loss.\n",
"ckpt_dir = '/tmp/regression_model/'\n",
"\n",
"with tf.Graph().as_default():\n",
Expand All @@ -244,7 +244,7 @@
" predictions, nodes = regression_model(inputs, is_training=True)\n",
"\n",
" # Add the loss function to the graph.\n",
" loss = slim.losses.sum_of_squares(predictions, targets)\n",
" loss = tf.losses.mean_squared_error(labels=targets, predictions=predictions)\n",
" \n",
" # The total loss is the uers's loss plus any regularization losses.\n",
" total_loss = slim.losses.get_total_loss()\n",
Expand Down Expand Up @@ -289,12 +289,12 @@
" predictions, end_points = regression_model(inputs, is_training=True)\n",
"\n",
" # Add multiple loss nodes.\n",
" sum_of_squares_loss = slim.losses.sum_of_squares(predictions, targets)\n",
" mean_squared_error_loss = tf.losses.mean_squared_error(labels=targets, predictions=predictions)\n",
" absolute_difference_loss = slim.losses.absolute_difference(predictions, targets)\n",
"\n",
" # The following two ways to compute the total loss are equivalent\n",
" regularization_loss = tf.add_n(slim.losses.get_regularization_losses())\n",
" total_loss1 = sum_of_squares_loss + absolute_difference_loss + regularization_loss\n",
" total_loss1 = mean_squared_error_loss + absolute_difference_loss + regularization_loss\n",
"\n",
" # Regularization Loss is included in the total loss by default.\n",
" # This is good for training, but not for testing.\n",
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