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TF Model Optimisation - Refinements
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‎00_Miscellaneous/model_optimisation/Tutorial - TensorFlow Model Optimisation for Serving - MNIST.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"WARNING:tensorflow:From <ipython-input-2-d053803e8488>:1: load_dataset (from tensorflow.contrib.learn.python.learn.datasets) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Please use tf.data.\n",
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"WARNING:tensorflow:From /Users/khalidsalama/Technology/python-venvs/py27-venv/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/__init__.py:80: load_mnist (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
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"WARNING:tensorflow:From /Users/khalidsalama/Technology/python-venvs/py27-venv/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:300: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
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"WARNING:tensorflow:From /Users/khalidsalama/Technology/python-venvs/py27-venv/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Please write your own downloading logic.\n",
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"WARNING:tensorflow:From /Users/khalidsalama/Technology/python-venvs/py27-venv/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Please use tf.data to implement this functionality.\n",
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"Extracting MNIST-data/train-images-idx3-ubyte.gz\n",
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"WARNING:tensorflow:From /Users/khalidsalama/Technology/python-venvs/py27-venv/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Please use tf.data to implement this functionality.\n",
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"Extracting MNIST-data/train-labels-idx1-ubyte.gz\n",
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"Extracting MNIST-data/t10k-images-idx3-ubyte.gz\n",
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"Extracting MNIST-data/t10k-labels-idx1-ubyte.gz\n",
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"WARNING:tensorflow:From /Users/khalidsalama/Technology/python-venvs/py27-venv/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: __init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
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"Extracting MNIST-data/t10k-labels-idx1-ubyte.gz\n"
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]
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}
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],
@@ -339,7 +318,7 @@
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" num_conv_layers=3, \n",
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" init_filters=64,\n",
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" dropout=0.2,\n",
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" max_traning_steps=1, #50\n",
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" max_traning_steps=50,\n",
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" eval_throttle_secs=10,\n",
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" learning_rate=1e-3\n",
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")\n",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Experiment started at 21:56:45\n",
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"Removing previous artifacts...\n",
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"Experiment started at 18:53:20\n",
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".......................................\n",
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"INFO:tensorflow:Using config: {'_save_checkpoints_secs': None, '_global_id_in_cluster': 0, '_session_config': None, '_keep_checkpoint_max': 3, '_tf_random_seed': 19830610, '_task_type': 'worker', '_train_distribute': None, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x1065c5790>, '_model_dir': 'models/mnist/cnn_classifier', '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_master': '', '_save_checkpoints_steps': 1000, '_keep_checkpoint_every_n_hours': 10000, '_evaluation_master': '', '_service': None, '_device_fn': None, '_save_summary_steps': 100, '_num_ps_replicas': 0}\n",
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"INFO:tensorflow:Using config: {'_save_checkpoints_secs': None, '_session_config': None, '_keep_checkpoint_max': 3, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x1175b4550>, '_evaluation_master': '', '_save_checkpoints_steps': 1000, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': 19830610, '_master': '', '_device_fn': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_model_dir': 'models/mnist/cnn_classifier', '_train_distribute': None, '_save_summary_steps': 100}\n",
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"INFO:tensorflow:Using config: {'_save_checkpoints_secs': None, '_global_id_in_cluster': 0, '_session_config': None, '_keep_checkpoint_max': 3, '_tf_random_seed': 19830610, '_task_type': 'worker', '_train_distribute': None, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x118556c10>, '_model_dir': 'models/mnist/cnn_classifier', '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_master': '', '_save_checkpoints_steps': 1000, '_keep_checkpoint_every_n_hours': 10000, '_evaluation_master': '', '_service': None, '_device_fn': None, '_save_summary_steps': 100, '_num_ps_replicas': 0}\n",
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"INFO:tensorflow:Using config: {'_save_checkpoints_secs': None, '_session_config': None, '_keep_checkpoint_max': 3, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x115dd0350>, '_evaluation_master': '', '_save_checkpoints_steps': 1000, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': 19830610, '_master': '', '_device_fn': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_model_dir': 'models/mnist/cnn_classifier', '_train_distribute': None, '_save_summary_steps': 100}\n",
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"INFO:tensorflow:Running training and evaluation locally (non-distributed).\n",
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"INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 1000 or save_checkpoints_secs None.\n",
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"INFO:tensorflow:Calling model_fn.\n",
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"INFO:tensorflow:Running local_init_op.\n",
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"INFO:tensorflow:Done running local_init_op.\n",
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"INFO:tensorflow:Saving checkpoints for 0 into models/mnist/cnn_classifier/model.ckpt.\n",
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"INFO:tensorflow:loss = 2.3013015, step = 1\n",
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"INFO:tensorflow:Saving checkpoints for 1 into models/mnist/cnn_classifier/model.ckpt.\n",
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"INFO:tensorflow:loss = 2.3067682, step = 1\n",
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"INFO:tensorflow:Saving checkpoints for 50 into models/mnist/cnn_classifier/model.ckpt.\n",
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"INFO:tensorflow:Calling model_fn.\n",
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"INFO:tensorflow:Calling model_fn.\n",
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"INFO:tensorflow:Done calling model_fn.\n",
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"INFO:tensorflow:Done calling model_fn.\n",
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"INFO:tensorflow:Starting evaluation at 2018-10-06-21:56:49\n",
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"INFO:tensorflow:Starting evaluation at 2018-10-07-18:53:45\n",
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"INFO:tensorflow:Graph was finalized.\n",
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"INFO:tensorflow:Restoring parameters from models/mnist/cnn_classifier/model.ckpt-1\n",
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"INFO:tensorflow:Restoring parameters from models/mnist/cnn_classifier/model.ckpt-50\n",
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"INFO:tensorflow:Running local_init_op.\n",
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"INFO:tensorflow:Done running local_init_op.\n",
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"INFO:tensorflow:Finished evaluation at 2018-10-06-21:57:03\n",
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"INFO:tensorflow:Saving dict for global step 1: accuracy = 0.1032, average_loss = 2.266044, global_step = 1, loss = 2.2660437, micro_accuracy = 0.1\n",
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"INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1: models/mnist/cnn_classifier/model.ckpt-1\n",
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"INFO:tensorflow:Loss for final step: 2.3013015.\n",
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"INFO:tensorflow:Finished evaluation at 2018-10-07-18:53:58\n",
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"INFO:tensorflow:Saving dict for global step 50: accuracy = 0.9387, average_loss = 0.20039336, global_step = 50, loss = 0.20039335, micro_accuracy = 0.9373829\n",
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"INFO:tensorflow:Saving 'checkpoint_path' summary for global step 50: models/mnist/cnn_classifier/model.ckpt-50\n",
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"INFO:tensorflow:Loss for final step: 0.15960121.\n",
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".......................................\n",
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"Experiment finished at 21:57:03\n",
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"Experiment finished at 18:53:59\n",
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"\n",
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"Experiment elapsed time: 18.031292 seconds\n"
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"Experiment elapsed time: 38.899845 seconds\n"
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]
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}
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