# -*- coding: utf-8 -*-
"""
Created on Wed Dec 20 00:57:49 2017

@author: YANG_HE
"""

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets(r'E:\python\mnist_data', one_hot=True)

import tensorflow as tf

# Parameters
learning_rate = 0.001
training_iters = 20
batch_size = 64
display_step = 2

# Network Parameters
n_input = 784 # MNIST data input (img shape: 28*28)
n_classes = 10 # MNIST total classes (0-9 digits)
dropout = 0.8 # Dropout, probability to keep units

# tf Graph input
x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
keep_prob = tf.placeholder(tf.float32) # dropout (keep probability)

# Create custom model
def conv2d(name, l_input, w, b):
    return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(l_input, w, strides=[1, 1, 1, 1], padding='SAME'),b), name=name)

def max_pool(name, l_input, k):
    return tf.nn.max_pool(l_input, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME', name=name)

def norm(name, l_input, lsize=4):
    return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name=name)

def customnet(_X, _weights, _biases, _dropout):
    # Reshape input picture
    _X = tf.reshape(_X, shape=[-1, 28, 28, 1])

    # Convolution Layer
    conv1 = conv2d('conv1', _X, _weights['wc1'], _biases['bc1'])
    # Max Pooling (down-sampling)
    pool1 = max_pool('pool1', conv1, k=2)
    # Apply Normalization
    norm1 = norm('norm1', pool1, lsize=4)
    # Apply Dropout
    norm1 = tf.nn.dropout(norm1, _dropout)
    #conv1 image show
    #tf.summary.image("conv1", conv1)
    # Convolution Layer
    conv2 = conv2d('conv2', norm1, _weights['wc2'], _biases['bc2'])
    # Max Pooling (down-sampling)
    pool2 = max_pool('pool2', conv2, k=2)
    # Apply Normalization
    norm2 = norm('norm2', pool2, lsize=4)
    # Apply Dropout
    norm2 = tf.nn.dropout(norm2, _dropout)

    # Convolution Layer
    conv3 = conv2d('conv3', norm2, _weights['wc3'], _biases['bc3'])
    # Max Pooling (down-sampling)
    pool3 = max_pool('pool3', conv3, k=2)
    # Apply Normalization
    norm3 = norm('norm3', pool3, lsize=4)
    # Apply Dropout
    norm3 = tf.nn.dropout(norm3, _dropout)
    #conv4
    conv4 = conv2d('conv4', norm3, _weights['wc4'], _biases['bc4'])
    # Max Pooling (down-sampling)
    pool4 = max_pool('pool4', conv4, k=2)
    # Apply Normalization
    norm4 = norm('norm4', pool4, lsize=4)
    # Apply Dropout
    norm4 = tf.nn.dropout(norm4, _dropout)
    # Fully connected layer
    dense1 = tf.reshape(norm4, [-1, _weights['wd1'].get_shape().as_list()[0]]) # Reshape conv3 output to fit dense layer input
    dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc1') # Relu activation

    dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc2') # Relu activation

    # Output, class prediction
    out = tf.matmul(dense2, _weights['out']) + _biases['out']
    return out

# Store layers weight & bias
weights = {
    'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64])),
    'wc2': tf.Variable(tf.random_normal([3, 3, 64, 128])),
    'wc3': tf.Variable(tf.random_normal([3, 3, 128, 256])),
    'wc4': tf.Variable(tf.random_normal([2, 2, 256, 512])),
    'wd1': tf.Variable(tf.random_normal([2*2*512, 1024])), 
    'wd2': tf.Variable(tf.random_normal([1024, 1024])),
    'out': tf.Variable(tf.random_normal([1024, 10]))
}
biases = {
    'bc1': tf.Variable(tf.random_normal([64])),
    'bc2': tf.Variable(tf.random_normal([128])),
    'bc3': tf.Variable(tf.random_normal([256])),
    'bc4': tf.Variable(tf.random_normal([512])),
    'bd1': tf.Variable(tf.random_normal([1024])),
    'bd2': tf.Variable(tf.random_normal([1024])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

# Construct model
pred = customnet(x, weights, biases, keep_prob)

# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# Evaluate model
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# Initializing the variables
init = tf.initialize_all_variables()
# 
tf.summary.scalar("loss", cost)
tf.summary.scalar("accuracy", accuracy)
# Merge all summaries to a single operator
merged_summary_op = tf.summary.merge_all()
# Launch the graph
with tf.Session() as sess:
    sess.run(init)
    summary_writer = tf.summary.FileWriter('E:/python/CNN_test/src/tensorflow/tensorboard/simple.log', graph_def=sess.graph_def)
    step = 1
    # Keep training until reach max iterations
    while step in range(10):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        # Fit training using batch data
        sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})
        if step % display_step == 0:
            # Calculate batch accuracy
            acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
            # Calculate batch loss
            loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
            print ("Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc))
            summary_str = sess.run(merged_summary_op, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
            summary_writer.add_summary(summary_str, step)
        step += 1
    print ("Optimization Finished!")
    # Calculate accuracy for 256 mnist test images
    print ("Testing Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.}))

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

import argparse
from datetime import datetime
import math
import sys
import time

from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf

FLAGS = None


def print_activations(t):
  print(t.op.name, ' ', t.get_shape().as_list())


def inference(images):
  """Build the AlexNet model.
  Args:
    images: Images Tensor
  Returns:
    pool5: the last Tensor in the convolutional component of AlexNet.
    parameters: a list of Tensors corresponding to the weights and biases of the
        AlexNet model.
  """
  parameters = []
  # conv1
  with tf.name_scope('conv1') as scope:
    kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 96], dtype=tf.float32,
                                             stddev=1e-1), name='weights')
    conv = tf.nn.conv2d(images, kernel, [1, 4, 4, 1], padding='SAME')
    biases = tf.Variable(tf.constant(0.0, shape=[96], dtype=tf.float32),
                         trainable=True, name='biases')
    bias = tf.nn.bias_add(conv, biases)
    conv1 = tf.nn.relu(bias, name=scope)
    print_activations(conv1)
    parameters += [kernel, biases]

  # lrn1
  # TODO(shlens, jiayq): Add a GPU version of local response normalization.

  # pool1
  pool1 = tf.nn.max_pool(conv1,
                         ksize=[1, 3, 3, 1],
                         strides=[1, 2, 2, 1],
                         padding='VALID',
                         name='pool1')
  print_activations(pool1)

  # conv2
  with tf.name_scope('conv2') as scope:
    kernel = tf.Variable(tf.truncated_normal([5, 5, 96, 256], dtype=tf.float32,
                                             stddev=1e-1), name='weights')
    conv = tf.nn.conv2d(pool1, kernel, [1, 1, 1, 1], padding='SAME')
    biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
                         trainable=True, name='biases')
    bias = tf.nn.bias_add(conv, biases)
    conv2 = tf.nn.relu(bias, name=scope)
    parameters += [kernel, biases]
  print_activations(conv2)

  # pool2
  pool2 = tf.nn.max_pool(conv2,
                         ksize=[1, 3, 3, 1],
                         strides=[1, 2, 2, 1],
                         padding='VALID',
                         name='pool2')
  print_activations(pool2)

  # conv3
  with tf.name_scope('conv3') as scope:
    kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 384],
                                             dtype=tf.float32,
                                             stddev=1e-1), name='weights')
    conv = tf.nn.conv2d(pool2, kernel, [1, 1, 1, 1], padding='SAME')
    biases = tf.Variable(tf.constant(0.0, shape=[384], dtype=tf.float32),
                         trainable=True, name='biases')
    bias = tf.nn.bias_add(conv, biases)
    conv3 = tf.nn.relu(bias, name=scope)
    parameters += [kernel, biases]
    print_activations(conv3)

  # conv4
  with tf.name_scope('conv4') as scope:
    kernel = tf.Variable(tf.truncated_normal([3, 3, 384, 384],
                                             dtype=tf.float32,
                                             stddev=1e-1), name='weights')
    conv = tf.nn.conv2d(conv3, kernel, [1, 1, 1, 1], padding='SAME')
    biases = tf.Variable(tf.constant(0.0, shape=[384], dtype=tf.float32),
                         trainable=True, name='biases')
    bias = tf.nn.bias_add(conv, biases)
    conv4 = tf.nn.relu(bias, name=scope)
    parameters += [kernel, biases]
    print_activations(conv4)

  # conv5
  with tf.name_scope('conv5') as scope:
    kernel = tf.Variable(tf.truncated_normal([3, 3, 384, 256],
                                             dtype=tf.float32,
                                             stddev=1e-1), name='weights')
    conv = tf.nn.conv2d(conv4, kernel, [1, 1, 1, 1], padding='SAME')
    biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
                         trainable=True, name='biases')
    bias = tf.nn.bias_add(conv, biases)
    conv5 = tf.nn.relu(bias, name=scope)
    parameters += [kernel, biases]
    print_activations(conv5)

  # pool5
  pool5 = tf.nn.max_pool(conv5,
                         ksize=[1, 3, 3, 1],
                         strides=[1, 2, 2, 1],
                         padding='VALID',
                         name='pool5')
  print_activations(pool5)

  return pool5, parameters


def time_tensorflow_run(session, target, info_string):
  """Run the computation to obtain the target tensor and print timing stats.
  Args:
    session: the TensorFlow session to run the computation under.
    target: the target Tensor that is passed to the session's run() function.
    info_string: a string summarizing this run, to be printed with the stats.
  Returns:
    None
  """
  num_steps_burn_in = 10
  total_duration = 0.0
  total_duration_squared = 0.0
  for i in xrange(FLAGS.num_batches + num_steps_burn_in):
    start_time = time.time()
    _ = session.run(target)
    duration = time.time() - start_time
    if i >= num_steps_burn_in:
      if not i % 10:
        print ('%s: step %d, duration = %.3f' %
               (datetime.now(), i - num_steps_burn_in, duration))
      total_duration += duration
      total_duration_squared += duration * duration
  mn = total_duration / FLAGS.num_batches
  vr = total_duration_squared / FLAGS.num_batches - mn * mn
  sd = math.sqrt(vr)
  print ('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %
         (datetime.now(), info_string, FLAGS.num_batches, mn, sd))



def run_benchmark():
  """Run the benchmark on AlexNet."""
  with tf.Graph().as_default():
    # Generate some dummy images.
    image_size = 224
    # Note that our padding definition is slightly different the cuda-convnet.
    # In order to force the model to start with the same activations sizes,
    # we add 3 to the image_size and employ VALID padding above.
    images = tf.Variable(tf.random_normal([FLAGS.batch_size,
                                           image_size,
                                           image_size, 3],
                                          dtype=tf.float32,
                                          stddev=1e-1))

    # Build a Graph that computes the logits predictions from the
    # inference model.
    pool5, parameters = inference(images)

    # Build an initialization operation.
    init = tf.global_variables_initializer()

    # Start running operations on the Graph.
    config = tf.ConfigProto()
    config.gpu_options.allocator_type = 'BFC'
    sess = tf.Session(config=config)
    sess.run(init)

    # Run the forward benchmark.
    time_tensorflow_run(sess, pool5, "Forward")

    # Add a simple objective so we can calculate the backward pass.
    objective = tf.nn.l2_loss(pool5)
    # Compute the gradient with respect to all the parameters.
    grad = tf.gradients(objective, parameters)
    # Run the backward benchmark.
    time_tensorflow_run(sess, grad, "Forward-backward")


def main(_):
  run_benchmark()


if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument(
      '--batch_size',
      type=int,
      default=128,
      help='Batch size.'
  )
  parser.add_argument(
      '--num_batches',
      type=int,
      default=100,
      help='Number of batches to run.'
  )
  FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)