Tensorflow tutorial trial

gistfile1.txt

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

# Imports
import numpy as np
import tensorflow as tf
from array import *
import os,cv2

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

def cnn_model_fn(features, labels, mode):

  """Model function for CNN."""
  # Input Layer
  print(labels.shape)
  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)     #HELPS PREVENT OVERFITTING

  # Logits Layer
  logits = tf.layers.dense(inputs=dropout, units=2)       #CHanged from 10 to 2 #FINAL LAYER
  print(logits.shape)
  predictions = {
      # Generate predictions (for PREDICT and EVAL mode)
      "classes": tf.argmax(input=logits, axis=1),
      # Add `softmax_tensor` to the 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)
  loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)

  # Configure the 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 metrics (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 training and eval data

  Names = [['C:\\Users\\xx\\Project\\training-images', 'train', 9490],
           ['C:\\Users\\xx\\Project\\test-images', 'test', 3175]]

  label_MAX = []
  datasets = []
  for name in Names:
      data_label = array('B')
      FileList = []
      for dirname in os.listdir(name[0]):

          path = os.path.join(name[0], dirname)
          for filename in os.listdir(path):
              if filename.endswith(".png"):
                  FileList.append(os.path.join(name[0], dirname, filename))
      print(FileList)
      for filename in FileList:
          label = int(filename.split('\\')[6])
          data_label.append(label)
      label_MAX.append(data_label)
      x_data = np.array([np.array(cv2.imread(filename)) for filename in FileList])
      pixels = x_data.flatten().reshape(name[2], 2352)
      pixels = pixels / 255.0
      datasets.append(pixels)
      for i in pixels[0]:
          print(i)

  #mnist = tf.contrib.learn.datasets.load_dataset("mnist")
  train_data = datasets[0]  # Returns np.array

  print(train_data.shape)


  train_labels = np.asarray(label_MAX[0], dtype=np.int32)


  print(train_labels.shape)

  eval_data = datasets[1]  # Returns np.array


  print(eval_data.shape)

  eval_labels = np.asarray(label_MAX[1], dtype=np.int32)


  print(eval_labels.shape)
  # Create the Estimator
  mnist_classifier = tf.estimator.Estimator(
      model_fn=cnn_model_fn, model_dir="C:\\Users\\MAHE\\Desktop\\Trial")

  # Set up logging for predictions
  # Log the values in the "Softmax" tensor with label "probabilities"
  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=300,
      num_epochs=None,
      shuffle=True)
  mnist_classifier.train(
      input_fn=train_input_fn,
      steps=100,
      hooks=[logging_hook])

  # Evaluate the model and print results
  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)

  pred_input_fn = tf.estimator.inputs.numpy_input_fn(           
      x={"x": eval_data},                                          
      shuffle=False)                                            
  pred_results = mnist_classifier.predict(input_fn=pred_input_fn)
  print(next(pred_results))                                     


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