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

import numpy as np
import tensorflow as tf

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

#APPLICATION LOGIC

#Typically CNN consists of stack of convolutional models each module consists of convolutional layer followed by pooling layer
#Last covolutional module is follwed by one or more dense layers. Last dense layer contains single node for each class
#in the model, with a soft max activation function to generate values between 0 to 1 for each node. So each node in last layer can be 
# seen as probability/likelihod of that class for an input image

def cnn_model_fn(features, labels, mode):
    """model function for CNN"""
    #input layer
    input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
    
    #convolutional layer #1
    conv1 = tf.layers.conv2d(inputs = input_layer,
                            filters = 32,
                            kernel_size = [5,5],
                            padding="same",
                            activation = tf.nn.relu)
    
    # pooling layer #1
    pool1 = tf.layers.max_pooling2d(inputs = conv1, pool_size=[2,2], strides = 2)
    
    #convolutional layer #2 and pooling layer #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)

    
    #dense layer
    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 == tf.estimator.ModeKeys.TRAIN)
    
    #logit layer
    logits = tf.layers.dense(inputs= dropout, units=10)
    
    predictions = {
        #generate preditions for oredict and eval mode
        "classes" : tf.argmax(input = logits, axis = 1),
        #add softmax tensor to teh graph. It is used for PREDICT and by the 'logging hook'
        "probabilities" : tf.nn.softmax(logits, name = "softmax_tensor")
    }
    
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
    
    #calculate LOSS for both train and eval modes
    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)
    
    #configure training Op (for TRAIN Mode)
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.GradientDescentOptimizer(learning_rate = 0.001)
        train_op = optimizer.minimize(loss = loss,
                                     global_step = tf.train.get_global_step())
        return tf.estimator.EstimatorSpec(mode=mode, loss=loss,train_op = train_op)

    #Add evaluation metric for eval mode
    eval_metric_ops = {
        "accuracy": tf.metrics.accuracy(labels=labels, predictions=predictions["classes"])
    }
    return tf.estimator.EstimatorSpec(mode = mode, loss = loss, eval_metric_ops=eval_metric_ops)

def main(unused_argv):
    #load traiing data and eval data
    mnist = tf.contrib.learn.datasets.load_dataset("mnist")
    train_data = mnist.train.images #returns nummpy array
    train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
    eval_data = mnist.test.images #returns nummpy array
    eval_labels = np.asarray(mnist.test.labels, dtype = np.int32)
    mnist_classifier = tf.estimator.Estimator(model_fn = cnn_model_fn,model_dir="/tmp/mnist_convnet_model")
    #add logging hook
    tensors_to_log = {"probabilities","softmax_tensor"}
    #logging_hook = tf.train.LoggingTensorHook(tensors = tensors_to_log,every_n_iter=50)
    #train the model                                          
    train_input_fn = tf.estimator.inputs.numpy_input_fn(x = {"x":train_data},
                                                       y=train_labels,
                                                       batch_size = 100,
                                                       num_epochs = None,
                                                       shuffle=True)
    mnist_classifier.train(input_fn = train_input_fn,
                          steps=1000)
                          #hooks=[logging_hook])
    eval_input_fn = tf.estimator.inputs.numpy_input_fn(
        x={"x":eval_data},
        y = eval_labels,
        num_epochs=1,
        shuffle=False
    )
    eval_results = mnist_classifier.evaluate(input_fn = eval_input_fn)
    print(eval_results)
                                              
if __name__ == "__main__":
    tf.app.run()
