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main.py
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main.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# Imports
import numpy as np
import tensorflow as tf
from ladder_model import ladder_fn
from tensorflow.examples.tutorials.mnist import input_data
tf.flags.DEFINE_string("output_dir", "", "Optional output dir.")
tf.flags.DEFINE_string("schedule", "train_and_evaluate", "Schedule.")
tf.flags.DEFINE_string("hparams", "", "Hyper parameters.")
FLAGS = tf.flags.FLAGS
tf.logging.set_verbosity(tf.logging.INFO)
BATCH_SIZE = 100
layer_params = list()
layer_params.append({'type': 'input',
'input_shape': None,
'output_shape': np.array([BATCH_SIZE, 784]),
'activation': lambda x: x,
'gamma': 10,
'noise': 0.25})
layer_params.append({'type': 'conv',
'input_shape': np.array([BATCH_SIZE, 28, 28, 1]),
'output_shape': np.array([BATCH_SIZE, 32, 32, 32]),
'kernel': 5,
'activation': tf.nn.relu,
'gamma': 0.1,
'noise': 0.04,
'center': True,
'scale': False})
layer_params.append({'type': 'maxpool',
'input_shape': np.array([BATCH_SIZE, 32, 32, 32]),
'output_shape': np.array([BATCH_SIZE, 16, 16, 32]),
'activation': lambda x: x,
'kernel': 2,
'gamma': 0.1,
'noise': 0.04,
'center': True,
'scale': False})
layer_params.append({'type': 'conv',
'input_shape': np.array([BATCH_SIZE, 16, 16, 32]),
'output_shape': np.array([BATCH_SIZE, 18, 18, 64]),
'kernel': 3,
'activation': tf.nn.relu,
'gamma': 0.1,
'noise': 0.04,
'center': True,
'scale': False})
layer_params.append({'type': 'conv',
'input_shape': np.array([BATCH_SIZE, 18, 18, 64]),
'output_shape': np.array([BATCH_SIZE, 20, 20, 64]),
'kernel': 3,
'activation': tf.nn.relu,
'gamma': 0.1,
'noise': 0.04,
'center': True,
'scale': False})
layer_params.append({'type': 'maxpool',
'input_shape': np.array([BATCH_SIZE, 20, 20, 64]),
'output_shape': np.array([BATCH_SIZE, 10, 10, 64]),
'activation': lambda x: x,
'kernel': 2,
'gamma': 0.1,
'noise': 0.04,
'center': True,
'scale': False})
layer_params.append({'type': 'conv',
'input_shape': np.array([BATCH_SIZE, 10, 10, 64]),
'output_shape': np.array([BATCH_SIZE, 12, 12, 128]),
'kernel': 3,
'activation': tf.nn.relu,
'gamma': 0.1,
'noise': 0.04,
'center': True,
'scale': False})
layer_params.append({'type': 'conv',
'input_shape': np.array([BATCH_SIZE, 12, 12, 128]),
'output_shape': np.array([BATCH_SIZE, 14, 14, 10]),
'kernel': 3,
'activation': tf.nn.relu,
'gamma': 0.1,
'noise': 0.04,
'center': True,
'scale': False})
layer_params.append({'type': 'meanpool',
'input_shape': np.array([BATCH_SIZE, 14, 14, 10]),
'output_shape': np.array([BATCH_SIZE, 1, 1, 10]),
'kernel': 14,
'activation': lambda x: x,
'gamma': 0.01,
'noise': 0.04,
'center': False,
'scale': False})
layer_params.append({'type': 'fc',
'input_shape': np.array([BATCH_SIZE, 10]),
'output_shape': np.array([BATCH_SIZE, 10]),
'activation': lambda x: x,
'gamma': 0.01,
'noise': 0.04,
'center': True,
'scale': True})
params = {'learning_rate': 0.001,
'x_mean': 0,
'x_std': 1,
'label_stats': np.asarray([0.5]),
'layer_params': layer_params}
def main(unused_argv):
mnist = input_data.read_data_sets("MNIST_data/")
train_data = mnist.train.images # Returns np.array
train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
eval_data = mnist.test.images # Returns np.array
eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"inputs": train_data},
y=train_labels,
batch_size=100,
num_epochs=None,
shuffle=True)
# Evaluate the model and print results
eval_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"inputs": eval_data},
y=eval_labels,
batch_size=100,
shuffle=False)
config = tf.estimator.RunConfig()
config.environment = None
nn = tf.estimator.Estimator(model_fn=ladder_fn, params=params, model_dir="/tmp/mnist_img", config=config)
train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn, max_steps=2000)
eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, throttle_secs=300)
tf.estimator.train_and_evaluate(nn, train_spec, eval_spec)
#nn.evaluate(eval_input_fn,)
# experiment = tf.contrib.learn.Experiment(estimator=nn,
# train_input_fn=train_input_fn,
# eval_input_fn=eval_input_fn,
# train_steps_per_iteration = 10,
# train_steps=2000
# )
#
# experiment.train_and_evaluate()
tf.app.run()