#! /usr/bin/env python3
#
def dogs_vs_cats1 ( ):

#*****************************************************************************80
#
## dogs_vs_cats1 classifies images of dogs and cats.
#
#  Discussion:
#
#    The data consists of jpeg images of dogs and cats.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    14 May 2019
#
#  Author:
#
#    The original text is by Francois Chollet;
#    some modifications by John Burkardt.
#
#  Reference:
#
#    Francois Chollet,
#    Deep Learning with Python,
#    Manning, 2018,
#    ISBN: 9781617294433.
#
  import keras
  import platform
  from keras import layers
  from keras import models

  print ( '' )
  print ( 'dogs_vs_cats1:' )
  print ( '  python version: %s' % ( platform.python_version ( ) ) )
  print ( '  keras version: %s' % ( keras.__version__ ) )
  print ( '  Use a convolutional neural model' )
  print ( '  to classify jpeg images of dogs and cats.' )
#
#  Build the model.
#
  from keras import models
  from keras import layers

  model = models.Sequential()
  model.add ( layers.Conv2D ( 32, ( 3, 3 ), activation = 'relu', input_shape = ( 150, 150, 3 ) ) )
  model.add ( layers.MaxPooling2D ( ( 2, 2 ) ) )
  model.add ( layers.Conv2D ( 64, ( 3, 3 ), activation = 'relu' ) )
  model.add ( layers.MaxPooling2D ( ( 2, 2 ) ) )
  model.add ( layers.Conv2D ( 128, ( 3, 3 ), activation = 'relu' ) )
  model.add ( layers.MaxPooling2D ( ( 2, 2 ) ) )
  model.add ( layers.Conv2D ( 128, ( 3, 3 ), activation = 'relu' ) )
  model.add ( layers.MaxPooling2D ( ( 2, 2 ) ) )
  model.add ( layers.Flatten ( ) )
  model.add ( layers.Dense ( 512, activation = 'relu' ) )
  model.add ( layers.Dense ( 1, activation = 'sigmoid' ) )

  model.summary ( )
#
#  Compile the model.
#
  from keras import optimizers

  model.compile ( \
    loss = 'binary_crossentropy', \
    optimizer = optimizers.RMSprop ( lr = 1.0E-04 ), \
    metrics = [ 'acc' ] )
#
#  Read the images.
#
  from keras.preprocessing.image import ImageDataGenerator

  train_datagen = ImageDataGenerator ( rescale = 1.0 / 255.0 )
  test_datagen = ImageDataGenerator ( rescale = 1.0 / 255.0 )

  train_dir = '/Users/burkardt/public_html/keras_src/dogs_vs_cats_data/train'

  train_generator = train_datagen.flow_from_directory ( \
    train_dir, \
    target_size = ( 150, 150 ), \
    batch_size = 20, \
    class_mode = 'binary' )

  validation_dir = '/Users/burkardt/public_html/keras_src/dogs_vs_cats_data/valid'

  validation_generator = train_datagen.flow_from_directory ( \
    validation_dir, \
    target_size = ( 150, 150 ), \
    batch_size = 20, \
    class_mode = 'binary' )
#
#  Look at output of generator, but only do one pass.
#
  for data_batch, labels_batch in train_generator:
    print ( 'data_batch.shape:', data_batch.shape )
    print ( 'labels_batch.shape:', labels_batch.shape )
    break
#
#  Fit the model using a batch generator.
#
  history = model.fit_generator ( \
    train_generator, \
    steps_per_epoch = 100, \
    epochs = 30, \
    validation_data = validation_generator, \
    validation_steps = 50 )
#
#  Save a copy of the model as an HDF5 file.
#
  model.save ( 'dogs_vs_cats1.h5' )
#
#  Plot the loss and accuracy.
#
  import matplotlib.pyplot as plt

  acc = history.history[ 'acc' ]
  val_acc = history.history[ 'val_acc' ]
  loss = history.history[ 'loss' ]
  val_loss = history.history[ 'val_loss' ]

  epochs = range ( 1, len ( acc ) + 1 )

  plt.plot ( epochs, acc, 'bo', label = 'Training acc' )
  plt.plot ( epochs, val_acc, 'b', label = 'Validation acc' )
  plt.title ( 'Training and validation accuracy' )
  plt.legend ( )
  filename = 'dogs_vs_cats1_tvacc.png'
  plt.savefig ( filename )
  print ( '' )
  print ( '  Graphics saved as "%s"' % ( filename ) )
  plt.show ( )

  plt.plot ( epochs, loss, 'ro', label = 'Training loss' )
  plt.plot ( epochs, val_loss, 'r', label = 'Validation loss' )
  plt.title ( 'Training and validation loss' )
  plt.legend ( )
  filename = 'dogs_vs_cats1_tvloss.png'
  plt.savefig ( filename )
  print ( '' )
  print ( '  Graphics saved as "%s"' % ( filename ) )
  plt.show ( )
#
#  Terminate.
#
  print ( '' )
  print ( 'dogs_vs_cats1:' )
  print ( '  Normal end of execution.' )
  return

def timestamp ( ):

#*****************************************************************************80
#
## TIMESTAMP prints the date as a timestamp.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license. 
#
#  Modified:
#
#    06 April 2013
#
#  Author:
#
#    John Burkardt
#
#  Parameters:
#
#    None
#
  import time

  t = time.time ( )
  print ( time.ctime ( t ) )

  return None

if ( __name__ == '__main__' ):
  timestamp ( )
  dogs_vs_cats1 ( )
  timestamp ( )