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Make cifar10 tutorial work with 0.12.x, fixing #817. #849

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Make cifar10 tutorial work with 0.12.x, fixing #817. #849

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14 changes: 7 additions & 7 deletions tutorials/image/cifar10/cifar10.py
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Original file line number Diff line number Diff line change
Expand Up @@ -90,8 +90,8 @@ def _activation_summary(x):
# Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
# session. This helps the clarity of presentation on tensorboard.
tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name)
tf.contrib.deprecated.histogram_summary(tensor_name + '/activations', x)
tf.contrib.deprecated.scalar_summary(tensor_name + '/sparsity',
tf.summary.histogram(tensor_name + '/activations', x)
tf.summary.scalar(tensor_name + '/sparsity',
tf.nn.zero_fraction(x))


Expand Down Expand Up @@ -316,8 +316,8 @@ def _add_loss_summaries(total_loss):
for l in losses + [total_loss]:
# Name each loss as '(raw)' and name the moving average version of the loss
# as the original loss name.
tf.contrib.deprecated.scalar_summary(l.op.name + ' (raw)', l)
tf.contrib.deprecated.scalar_summary(l.op.name, loss_averages.average(l))
tf.summary.scalar(l.op.name + ' (raw)', l)
tf.summary.scalar(l.op.name, loss_averages.average(l))

return loss_averages_op

Expand Down Expand Up @@ -345,7 +345,7 @@ def train(total_loss, global_step):
decay_steps,
LEARNING_RATE_DECAY_FACTOR,
staircase=True)
tf.contrib.deprecated.scalar_summary('learning_rate', lr)
tf.summary.scalar('learning_rate', lr)

# Generate moving averages of all losses and associated summaries.
loss_averages_op = _add_loss_summaries(total_loss)
Expand All @@ -360,12 +360,12 @@ def train(total_loss, global_step):

# Add histograms for trainable variables.
for var in tf.trainable_variables():
tf.contrib.deprecated.histogram_summary(var.op.name, var)
tf.summary.histogram(var.op.name, var)

# Add histograms for gradients.
for grad, var in grads:
if grad is not None:
tf.contrib.deprecated.histogram_summary(var.op.name + '/gradients', grad)
tf.summary.histogram(var.op.name + '/gradients', grad)

# Track the moving averages of all trainable variables.
variable_averages = tf.train.ExponentialMovingAverage(
Expand Down
16 changes: 6 additions & 10 deletions tutorials/image/cifar10/cifar10_input.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,17 +80,14 @@ class CIFAR10Record(object):
result.key, value = reader.read(filename_queue)

# Convert from a string to a vector of uint8 that is record_bytes long.
record_bytes = tf.decode_raw(value, tf.uint8)

value_bytes = tf.decode_raw(value, tf.uint8)
# The first bytes represent the label, which we convert from uint8->int32.
result.label = tf.cast(
tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32)
result.label = tf.cast(tf.slice(value_bytes, [0], [label_bytes]), tf.int32)
result.label.set_shape([1])

# The remaining bytes after the label represent the image, which we reshape
# from [depth * height * width] to [depth, height, width].
depth_major = tf.reshape(
tf.strided_slice(record_bytes, [label_bytes],
[label_bytes + image_bytes]),
depth_major = tf.reshape(tf.slice(value_bytes, [label_bytes], [image_bytes]),
[result.depth, result.height, result.width])
# Convert from [depth, height, width] to [height, width, depth].
result.uint8image = tf.transpose(depth_major, [1, 2, 0])
Expand Down Expand Up @@ -132,7 +129,7 @@ def _generate_image_and_label_batch(image, label, min_queue_examples,
capacity=min_queue_examples + 3 * batch_size)

# Display the training images in the visualizer.
tf.contrib.deprecated.image_summary('images', images)
tf.summary.image('images', images)

return images, tf.reshape(label_batch, [batch_size])

Expand Down Expand Up @@ -183,9 +180,8 @@ def distorted_inputs(data_dir, batch_size):
# Subtract off the mean and divide by the variance of the pixels.
float_image = tf.image.per_image_standardization(distorted_image)

# Set the shapes of tensors.
# Set the shape of the tensor.
float_image.set_shape([height, width, 3])
read_input.label.set_shape([1])

# Ensure that the random shuffling has good mixing properties.
min_fraction_of_examples_in_queue = 0.4
Expand Down