diff options
Diffstat (limited to 'tensorflow/examples/image_retraining')
-rw-r--r-- | tensorflow/examples/image_retraining/retrain.py | 26 |
1 files changed, 13 insertions, 13 deletions
diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py index d52a23fd15..4f06cb8add 100644 --- a/tensorflow/examples/image_retraining/retrain.py +++ b/tensorflow/examples/image_retraining/retrain.py @@ -647,17 +647,17 @@ def add_input_distortions(flip_left_right, random_crop, random_scale, return jpeg_data, distort_result -def variable_summaries(var, name): +def variable_summaries(var): """Attach a lot of summaries to a Tensor (for TensorBoard visualization).""" with tf.name_scope('summaries'): mean = tf.reduce_mean(var) - tf.scalar_summary('mean/' + name, mean) + tf.summary.scalar('mean', mean) with tf.name_scope('stddev'): stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean))) - tf.scalar_summary('stddev/' + name, stddev) - tf.scalar_summary('max/' + name, tf.reduce_max(var)) - tf.scalar_summary('min/' + name, tf.reduce_min(var)) - tf.histogram_summary(name, var) + tf.summary.scalar('stddev', stddev) + tf.summary.scalar('max', tf.reduce_max(var)) + tf.summary.scalar('min', tf.reduce_min(var)) + tf.summary.histogram('histogram', var) def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor): @@ -695,23 +695,23 @@ def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor): with tf.name_scope(layer_name): with tf.name_scope('weights'): layer_weights = tf.Variable(tf.truncated_normal([BOTTLENECK_TENSOR_SIZE, class_count], stddev=0.001), name='final_weights') - variable_summaries(layer_weights, layer_name + '/weights') + variable_summaries(layer_weights) with tf.name_scope('biases'): layer_biases = tf.Variable(tf.zeros([class_count]), name='final_biases') - variable_summaries(layer_biases, layer_name + '/biases') + variable_summaries(layer_biases) with tf.name_scope('Wx_plus_b'): logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases - tf.histogram_summary(layer_name + '/pre_activations', logits) + tf.summary.histogram('pre_activations', logits) final_tensor = tf.nn.softmax(logits, name=final_tensor_name) - tf.histogram_summary(final_tensor_name + '/activations', final_tensor) + tf.summary.histogram('activations', final_tensor) with tf.name_scope('cross_entropy'): cross_entropy = tf.nn.softmax_cross_entropy_with_logits( logits, ground_truth_input) with tf.name_scope('total'): cross_entropy_mean = tf.reduce_mean(cross_entropy) - tf.scalar_summary('cross entropy', cross_entropy_mean) + tf.summary.scalar('cross_entropy', cross_entropy_mean) with tf.name_scope('train'): train_step = tf.train.GradientDescentOptimizer(FLAGS.learning_rate).minimize( @@ -738,7 +738,7 @@ def add_evaluation_step(result_tensor, ground_truth_tensor): tf.argmax(ground_truth_tensor, 1)) with tf.name_scope('accuracy'): evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) - tf.scalar_summary('accuracy', evaluation_step) + tf.summary.scalar('accuracy', evaluation_step) return evaluation_step @@ -792,7 +792,7 @@ def main(_): evaluation_step = add_evaluation_step(final_tensor, ground_truth_input) # Merge all the summaries and write them out to /tmp/retrain_logs (by default) - merged = tf.merge_all_summaries() + merged = tf.summary.merge_all() train_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/train', sess.graph) validation_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/validation') |