from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import tensorflow as tf

from tensorflow.contrib import learn
from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib

tf.logging.set_verbosity(tf.logging.INFO)


def cnn_model_fn(features, labels, mode):

	input_layer = tf.reshape(features, [-1, 28, 28, 1])

	conv1 = tf.layers.conv2d(
		inputs=input_layer,
		filters=32,
		kernel_size=[5,5],
		padding="same",
		activation=tf.nn.relu)

	
	pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2,2], strides=2)

	conv2 = tf.layers.conv2d(
		inputs=pool1,
		filters=64,
		kernel_size=[5,5],
		padding="same",
		activation=tf.nn.relu)

	pool2 = tf.layers.max_pooling2d(inputs=conv2,pool_size=[2,2], strides=2)

	pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
	dense = tf.layers.dense(inputs=pool2_flat,units=1024, activation=tf.nn.relu)
	dropout = tf.layers.dropout(inputs=dense,rate=0.4, training=mode==learn.ModeKeys.TRAIN)

	logits = tf.layers.dense(inputs=dropout, units=10)

	loss = None
	train_op = None

	if mode != learn.ModeKeys.INFER:
		onehot_labels = tf.one_hot(indices=tf.cast(labels,tf.int32), depth=10)
		loss = tf.losses.softmax_cross_entropy(onehot_labels=onehot_labels,logits=logits)

	if mode == learn.ModeKeys.TRAIN:
		train_op = tf.contrib.layers.optimize_loss(
			loss=loss,
			global_step=tf.contrib.framework.get_global_step(),
			learning_rate=0.001,
			optimizer="SGD")
	
	predictions = {

		"classes": tf.argmax(
			input=logits, axis=1),
		"probabilities": tf.nn.softmax(
			logits, name="softmax_tensor")
	}
	

	return model_fn_lib.ModelFnOps(mode=mode,predictions=predictions,loss=loss,train_op=train_op)



def main(unused_argv):
	mnist = learn.datasets.load_dataset("mnist")
	train_data = mnist.train.images
	train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
	eval_data = mnist.test.images
	eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)

	mnist_classifier = learn.Estimator(model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")

	tensors_to_log = {"probabilities": "softmax_tensor"}
	logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log, every_n_iter=50)

	mnist_classifier.fit(
		x=train_data,
		y=train_labels,
		batch_size=100,
		steps=20000,
		monitors=[logging_hook])
	
	metrics = {
		"accuracy":
			learn.MetricSpec(metric_fn=tf.metrics.accuracy, prediction_key="classes"),
	}

	eval_results = mnist_classifer.evaluate(x=eval_data,y=eval_labels, metrics=metrics)
	print(eval_results)


if __name__ == "__main__":
	tf.app.run()