In [1]:
import os
import matplotlib.pyplot as plt
import numpy as np
import shutil
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.python.feature_column import feature_column
print(tf.__version__)
1.4.1
In [34]:
class FLAGS():
pass
FLAGS.model_dir = './trained_models'
FLAGS.data_dir = './mnist_data'
FLAGS.model_name = 'auto-encoder-03'
FLAGS.batch_size = 100
FLAGS.encoder_hidden_units = [50]
FLAGS.learning_rate = 0.01
FLAGS.l2_reg = 0.0001
FLAGS.noise_level = 0.0
FLAGS.dropout_rate = 0.1
FLAGS.resume_training = False
FLAGS.max_steps = 10000
FLAGS.eval_steps = 100
In [3]:
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
Extracting ./mnist_data/train-images-idx3-ubyte.gz
Extracting ./mnist_data/train-labels-idx1-ubyte.gz
Extracting ./mnist_data/t10k-images-idx3-ubyte.gz
Extracting ./mnist_data/t10k-labels-idx1-ubyte.gz
In [4]:
def generate_input_fn(mnist_data,
mode=tf.estimator.ModeKeys.EVAL,
batch_size=1):
def input_fn():
if mode == tf.estimator.ModeKeys.EVAL:
images = mnist.validation.images
labels = images
elif mode == tf.estimator.ModeKeys.TRAIN:
images = mnist.train.images
labels = images
else:
assert(False)
is_shuffle = (mode == tf.estimator.ModeKeys.TRAIN)
return tf.estimator.inputs.numpy_input_fn(
batch_size=batch_size,
shuffle=is_shuffle,
x={'images': images},
y=labels)
return input_fn()
In [5]:
def get_feature_columns():
feature_columns = {
'images': tf.feature_column.numeric_column('images', (784,))
}
return feature_columns
In [6]:
def get_estimator(run_config, hparams):
def _model_fn(features, labels, mode, params):
encoder_hidden_units = params.encoder_hidden_units
decoder_hidden_units = encoder_hidden_units
# Remove a middle unit if length of encoder hidden units exceeds 1.
if len(encoder_hidden_units) > 1:
decoder_hidden_units.reverse()
decoder_hidden_units.pop(0)
output_layer_size = 784
l2_regularizer = tf.contrib.layers.l2_regularizer(scale=params.l2_reg)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
with tf.name_scope('input'):
feature_columns = list(get_feature_columns().values())
input_layer = tf.feature_column.input_layer(
features=features, feature_columns=feature_columns)
with tf.name_scope('noisy_input'):
# Adding Gaussian Noise to input layer
noisy_input_layer = input_layer + (
params.noise_level * tf.random_normal(tf.shape(input_layer)))
# Dropout layer
dropout_layer = tf.layers.dropout(
inputs=noisy_input_layer, rate=params.dropout_rate, training=is_training)
with tf.name_scope('encoder'):
# Encoder layers stack
encoding_hidden_layers = tf.contrib.layers.stack(
inputs=dropout_layer,
layer=tf.contrib.layers.fully_connected,
stack_args=encoder_hidden_units,
weights_regularizer = l2_regularizer,
activation_fn=tf.nn.relu)
with tf.name_scope('decoder'):
# Decoder layers stack
decoding_hidden_layers = tf.contrib.layers.stack(
inputs=encoding_hidden_layers,
layer=tf.contrib.layers.fully_connected,
stack_args=decoder_hidden_units,
weights_regularizer=l2_regularizer,
activation_fn=tf.nn.relu)
# Output (reconstructured) layer
output_layer = tf.layers.dense(inputs=decoding_hidden_layers,
units=output_layer_size,
activation=None)
# Encoding output (i.e., extracted features) reshaped
encoding_output = tf.squeeze(encoding_hidden_layers)
# Reconstruction output reshaped (for loss calculation)
reconstruction_output = tf.squeeze(output_layer)
# Provide an estimator spec for `ModeKeys.PREDICT`.
if mode == tf.estimator.ModeKeys.PREDICT:
# Convert predicted_indices back into strings.
predictions = {
'encoding': encoding_output,
'reconstruction': reconstruction_output,
}
export_outputs = {
'predict': tf.estimator.export.PredictOutput(predictions)
}
# Provide an estimator spec for `ModeKeys.PREDICT` modes.
return tf.estimator.EstimatorSpec(
mode, predictions=predictions, export_outputs=export_outputs)
with tf.name_scope('loss'):
# Define loss based on reconstruction and regularization.
loss = tf.losses.mean_squared_error(tf.squeeze(input_layer),
reconstruction_output)
loss = loss + tf.losses.get_regularization_loss()
# Create optimizer
optimizer = tf.train.AdamOptimizer(params.learning_rate)
# Create training operation
train_op = optimizer.minimize(loss=loss,
global_step=tf.train.get_global_step())
# Calculate root mean squared error as additional metric.
eval_metric_ops = {
'rmse': tf.metrics.root_mean_squared_error(tf.squeeze(input_layer),
reconstruction_output)
}
# Provide an estimator spec for `ModeKeys.EVAL` and `ModeKeys.TRAIN` modes.
estimator_spec = tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops)
return estimator_spec
return tf.estimator.Estimator(model_fn=_model_fn, params=hparams, config=run_config)
In [7]:
def serving_input_fn():
receiver_tensor = {'images': tf.placeholder(shape=[None, 784], dtype=tf.float32)}
features = receiver_tensor
return tf.estimator.export.ServingInputReceiver(features, receiver_tensor)
In [43]:
run_config = tf.estimator.RunConfig(
model_dir=os.path.join(FLAGS.model_dir, FLAGS.model_name),
save_checkpoints_steps=1000,
#save_checkpoints_secs=None,
keep_checkpoint_max=5,
tf_random_seed=19851211
)
hparams = tf.contrib.training.HParams(
batch_size=FLAGS.batch_size,
encoder_hidden_units=FLAGS.encoder_hidden_units,
learning_rate=FLAGS.learning_rate,
l2_reg=FLAGS.l2_reg,
noise_level=FLAGS.noise_level,
dropout_rate=FLAGS.dropout_rate)
estimator = get_estimator(run_config, hparams)
train_spec = tf.estimator.TrainSpec(
input_fn=generate_input_fn(mnist,
mode=tf.estimator.ModeKeys.TRAIN,
batch_size=FLAGS.batch_size),
max_steps=FLAGS.max_steps, hooks=None)
exporter = tf.estimator.LatestExporter(
name='Servo',
serving_input_receiver_fn=serving_input_fn,
exports_to_keep=5)
eval_spec = tf.estimator.EvalSpec(
input_fn=generate_input_fn(mnist,
mode=tf.estimator.ModeKeys.TRAIN,
batch_size=FLAGS.batch_size),
steps=FLAGS.eval_steps, exporters=exporter)
if not FLAGS.resume_training:
print('Removing previous artifacts...')
shutil.rmtree(os.path.join(FLAGS.model_dir, FLAGS.model_name), ignore_errors=True)
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
INFO:tensorflow:Using config: {'_save_checkpoints_secs': None, '_session_config': None, '_keep_checkpoint_max': 5, '_tf_random_seed': 19851211, '_task_type': 'worker', '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fd81dbfaf50>, '_model_dir': './trained_models/auto-encoder-03', '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_master': '', '_save_checkpoints_steps': 1000, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_save_summary_steps': 100, '_num_ps_replicas': 0}
Removing previous artifacts...
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after 600 secs (eval_spec.throttle_secs) or training is finished.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Saving checkpoints for 1 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:loss = 0.126436, step = 1
INFO:tensorflow:global_step/sec: 242.243
INFO:tensorflow:loss = 0.0352383, step = 101 (0.414 sec)
INFO:tensorflow:global_step/sec: 292.135
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INFO:tensorflow:global_step/sec: 299.031
INFO:tensorflow:loss = 0.0323316, step = 501 (0.334 sec)
INFO:tensorflow:Saving checkpoints for 550 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0301196.
INFO:tensorflow:Starting evaluation at 2017-12-19-05:17:17
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-550
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INFO:tensorflow:Finished evaluation at 2017-12-19-05:17:17
INFO:tensorflow:Saving dict for global step 550: global_step = 550, loss = 0.0308417, rmse = 0.174023
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-550
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:SavedModel written to: ./trained_models/auto-encoder-03/export/Servo/temp-1513660637/saved_model.pb
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-550
INFO:tensorflow:Saving checkpoints for 551 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:loss = 0.0320761, step = 551
INFO:tensorflow:global_step/sec: 230.639
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INFO:tensorflow:Saving checkpoints for 1100 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0307637.
INFO:tensorflow:Starting evaluation at 2017-12-19-05:17:21
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-1100
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INFO:tensorflow:Finished evaluation at 2017-12-19-05:17:21
INFO:tensorflow:Saving dict for global step 1100: global_step = 1100, loss = 0.030158, rmse = 0.172074
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-1100
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:SavedModel written to: ./trained_models/auto-encoder-03/export/Servo/temp-1513660641/saved_model.pb
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-1100
INFO:tensorflow:Saving checkpoints for 1101 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:loss = 0.0311392, step = 1101
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INFO:tensorflow:Saving checkpoints for 1650 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0295872.
INFO:tensorflow:Starting evaluation at 2017-12-19-05:17:25
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-1650
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INFO:tensorflow:Finished evaluation at 2017-12-19-05:17:25
INFO:tensorflow:Saving dict for global step 1650: global_step = 1650, loss = 0.0293453, rmse = 0.169746
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-1650
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:SavedModel written to: ./trained_models/auto-encoder-03/export/Servo/temp-1513660645/saved_model.pb
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-1650
INFO:tensorflow:Saving checkpoints for 1651 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:loss = 0.0301988, step = 1651
INFO:tensorflow:global_step/sec: 239.534
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INFO:tensorflow:Saving checkpoints for 2200 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0302706.
INFO:tensorflow:Starting evaluation at 2017-12-19-05:17:29
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INFO:tensorflow:Finished evaluation at 2017-12-19-05:17:29
INFO:tensorflow:Saving dict for global step 2200: global_step = 2200, loss = 0.0287797, rmse = 0.168097
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-2200
INFO:tensorflow:Assets added to graph.
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INFO:tensorflow:SavedModel written to: ./trained_models/auto-encoder-03/export/Servo/temp-1513660649/saved_model.pb
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-2200
INFO:tensorflow:Saving checkpoints for 2201 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:loss = 0.0281317, step = 2201
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INFO:tensorflow:Saving checkpoints for 2750 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0315566.
INFO:tensorflow:Starting evaluation at 2017-12-19-05:17:33
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INFO:tensorflow:Finished evaluation at 2017-12-19-05:17:33
INFO:tensorflow:Saving dict for global step 2750: global_step = 2750, loss = 0.0285628, rmse = 0.167505
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-2750
INFO:tensorflow:Assets added to graph.
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INFO:tensorflow:SavedModel written to: ./trained_models/auto-encoder-03/export/Servo/temp-1513660654/saved_model.pb
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-2750
INFO:tensorflow:Saving checkpoints for 2751 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:loss = 0.0274262, step = 2751
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INFO:tensorflow:Saving checkpoints for 3300 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0295714.
INFO:tensorflow:Starting evaluation at 2017-12-19-05:17:37
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INFO:tensorflow:Finished evaluation at 2017-12-19-05:17:38
INFO:tensorflow:Saving dict for global step 3300: global_step = 3300, loss = 0.0288564, rmse = 0.168428
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-3300
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:SavedModel written to: ./trained_models/auto-encoder-03/export/Servo/temp-1513660658/saved_model.pb
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-3300
INFO:tensorflow:Saving checkpoints for 3301 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:loss = 0.0295795, step = 3301
INFO:tensorflow:global_step/sec: 225.788
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INFO:tensorflow:global_step/sec: 293.924
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INFO:tensorflow:Saving checkpoints for 3850 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0283907.
INFO:tensorflow:Starting evaluation at 2017-12-19-05:17:41
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INFO:tensorflow:Finished evaluation at 2017-12-19-05:17:42
INFO:tensorflow:Saving dict for global step 3850: global_step = 3850, loss = 0.0289913, rmse = 0.168851
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-3850
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:SavedModel written to: ./trained_models/auto-encoder-03/export/Servo/temp-1513660662/saved_model.pb
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-3850
INFO:tensorflow:Saving checkpoints for 3851 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:loss = 0.0283035, step = 3851
INFO:tensorflow:global_step/sec: 219.757
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INFO:tensorflow:global_step/sec: 292.802
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INFO:tensorflow:Saving checkpoints for 4400 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0287182.
INFO:tensorflow:Starting evaluation at 2017-12-19-05:17:45
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INFO:tensorflow:Saving dict for global step 4400: global_step = 4400, loss = 0.028912, rmse = 0.168616
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INFO:tensorflow:SavedModel written to: ./trained_models/auto-encoder-03/export/Servo/temp-1513660666/saved_model.pb
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-4400
INFO:tensorflow:Saving checkpoints for 4401 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:loss = 0.0293957, step = 4401
INFO:tensorflow:global_step/sec: 238.683
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INFO:tensorflow:Saving checkpoints for 4950 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0291003.
INFO:tensorflow:Starting evaluation at 2017-12-19-05:17:49
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INFO:tensorflow:Saving dict for global step 4950: global_step = 4950, loss = 0.0282369, rmse = 0.166616
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-4950
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INFO:tensorflow:SavedModel written to: ./trained_models/auto-encoder-03/export/Servo/temp-1513660670/saved_model.pb
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-4950
INFO:tensorflow:Saving checkpoints for 4951 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:loss = 0.0294445, step = 4951
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INFO:tensorflow:Saving checkpoints for 5500 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0285228.
INFO:tensorflow:Starting evaluation at 2017-12-19-05:17:53
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INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-5500
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INFO:tensorflow:SavedModel written to: ./trained_models/auto-encoder-03/export/Servo/temp-1513660673/saved_model.pb
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INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-5500
INFO:tensorflow:Saving checkpoints for 5501 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:loss = 0.029025, step = 5501
INFO:tensorflow:global_step/sec: 220.233
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INFO:tensorflow:Saving checkpoints for 6050 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:Loss for final step: 0.029898.
INFO:tensorflow:Starting evaluation at 2017-12-19-05:17:57
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INFO:tensorflow:Finished evaluation at 2017-12-19-05:17:57
INFO:tensorflow:Saving dict for global step 6050: global_step = 6050, loss = 0.0281495, rmse = 0.166397
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-6050
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:SavedModel written to: ./trained_models/auto-encoder-03/export/Servo/temp-1513660677/saved_model.pb
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-6050
INFO:tensorflow:Saving checkpoints for 6051 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:loss = 0.0311884, step = 6051
INFO:tensorflow:global_step/sec: 239.192
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INFO:tensorflow:Saving checkpoints for 6600 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0286637.
INFO:tensorflow:Starting evaluation at 2017-12-19-05:18:01
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INFO:tensorflow:Saving dict for global step 6600: global_step = 6600, loss = 0.028699, rmse = 0.168007
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INFO:tensorflow:SavedModel written to: ./trained_models/auto-encoder-03/export/Servo/temp-1513660681/saved_model.pb
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-6600
INFO:tensorflow:Saving checkpoints for 6601 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:loss = 0.03031, step = 6601
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INFO:tensorflow:Saving checkpoints for 7150 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0291337.
INFO:tensorflow:Starting evaluation at 2017-12-19-05:18:05
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INFO:tensorflow:Finished evaluation at 2017-12-19-05:18:06
INFO:tensorflow:Saving dict for global step 7150: global_step = 7150, loss = 0.0282492, rmse = 0.166701
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-7150
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INFO:tensorflow:SavedModel written to: ./trained_models/auto-encoder-03/export/Servo/temp-1513660686/saved_model.pb
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-7150
INFO:tensorflow:Saving checkpoints for 7151 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:loss = 0.0279439, step = 7151
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INFO:tensorflow:Saving checkpoints for 7700 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0283102.
INFO:tensorflow:Starting evaluation at 2017-12-19-05:18:09
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INFO:tensorflow:Finished evaluation at 2017-12-19-05:18:10
INFO:tensorflow:Saving dict for global step 7700: global_step = 7700, loss = 0.0288634, rmse = 0.168499
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-7700
INFO:tensorflow:Assets added to graph.
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INFO:tensorflow:SavedModel written to: ./trained_models/auto-encoder-03/export/Servo/temp-1513660690/saved_model.pb
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-7700
INFO:tensorflow:Saving checkpoints for 7701 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:loss = 0.0280608, step = 7701
INFO:tensorflow:global_step/sec: 214.56
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INFO:tensorflow:Saving checkpoints for 8250 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0297779.
INFO:tensorflow:Starting evaluation at 2017-12-19-05:18:13
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INFO:tensorflow:Finished evaluation at 2017-12-19-05:18:14
INFO:tensorflow:Saving dict for global step 8250: global_step = 8250, loss = 0.0283197, rmse = 0.166856
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-8250
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:SavedModel written to: ./trained_models/auto-encoder-03/export/Servo/temp-1513660694/saved_model.pb
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-8250
INFO:tensorflow:Saving checkpoints for 8251 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:loss = 0.0291794, step = 8251
INFO:tensorflow:global_step/sec: 203.21
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INFO:tensorflow:global_step/sec: 288.984
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INFO:tensorflow:Saving checkpoints for 8800 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0287029.
INFO:tensorflow:Starting evaluation at 2017-12-19-05:18:17
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INFO:tensorflow:Finished evaluation at 2017-12-19-05:18:18
INFO:tensorflow:Saving dict for global step 8800: global_step = 8800, loss = 0.0281129, rmse = 0.166277
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-8800
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:SavedModel written to: ./trained_models/auto-encoder-03/export/Servo/temp-1513660698/saved_model.pb
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-8800
INFO:tensorflow:Saving checkpoints for 8801 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:loss = 0.0293703, step = 8801
INFO:tensorflow:global_step/sec: 232.315
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INFO:tensorflow:global_step/sec: 298.987
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INFO:tensorflow:Saving checkpoints for 9350 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0297412.
INFO:tensorflow:Starting evaluation at 2017-12-19-05:18:21
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INFO:tensorflow:Saving dict for global step 9350: global_step = 9350, loss = 0.0286434, rmse = 0.167871
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-9350
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INFO:tensorflow:loss = 0.0301896, step = 9351
INFO:tensorflow:global_step/sec: 201.08
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INFO:tensorflow:Saving checkpoints for 9900 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:Loss for final step: 0.029715.
INFO:tensorflow:Starting evaluation at 2017-12-19-05:18:25
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INFO:tensorflow:Saving dict for global step 9900: global_step = 9900, loss = 0.0279965, rmse = 0.165989
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INFO:tensorflow:loss = 0.030422, step = 9901
INFO:tensorflow:Saving checkpoints for 10000 into ./trained_models/auto-encoder-03/model.ckpt.
INFO:tensorflow:Loss for final step: 0.0300649.
INFO:tensorflow:Starting evaluation at 2017-12-19-05:18:28
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INFO:tensorflow:Finished evaluation at 2017-12-19-05:18:28
INFO:tensorflow:Saving dict for global step 10000: global_step = 10000, loss = 0.0286079, rmse = 0.167836
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/model.ckpt-10000
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In [44]:
export_dir = os.path.join(FLAGS.model_dir, FLAGS.model_name, 'export/Servo/')
saved_model_dir = os.path.join(export_dir, os.listdir(export_dir)[-1])
predictor_fn = tf.contrib.predictor.from_saved_model(
export_dir = saved_model_dir,
signature_def_key='predict')
output = predictor_fn({'images': mnist.test.images})
INFO:tensorflow:Restoring parameters from ./trained_models/auto-encoder-03/export/Servo/1513660694/variables/variables
In [47]:
%matplotlib inline
x = np.random.randint(len(mnist.test.images))
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10,4))
ax1.set_title('original image')
ax1.imshow(mnist.test.images[x].reshape(28,28))
ax2.set_title('reconstructured image')
ax2.imshow(output['reconstruction'][x].reshape(28,28))
plt.show()
Content source: GoogleCloudPlatform/tf-estimator-tutorials
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