helper_functions.py

### We create a bunch of helpful functions throughout the course.
### Storing them here so they're easily accessible.

import tensorflow as tf

# Create a function to import an image and resize it to be able to be used with our model
def load_and_prep_image(filename, img_shape=224, scale=True):
  """
  Reads in an image from filename, turns it into a tensor and reshapes into
  (224, 224, 3).

  Parameters
  ----------
  filename (str): string filename of target image
  img_shape (int): size to resize target image to, default 224
  scale (bool): whether to scale pixel values to range(0, 1), default True
  """
  # Read in the image
  img = tf.io.read_file(filename)
  # Decode it into a tensor
  img = tf.image.decode_jpeg(img)
  # Resize the image
  img = tf.image.resize(img, [img_shape, img_shape])
  if scale:
    # Rescale the image (get all values between 0 and 1)
    return img/255.
  else:
    return img

# Note: The following confusion matrix code is a remix of Scikit-Learn's 
# plot_confusion_matrix function - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html
import itertools
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import confusion_matrix

# Our function needs a different name to sklearn's plot_confusion_matrix
def make_confusion_matrix(y_true, y_pred, classes=None, figsize=(10, 10), text_size=15, norm=False, savefig=False): 
  """Makes a labelled confusion matrix comparing predictions and ground truth labels.

  If classes is passed, confusion matrix will be labelled, if not, integer class values
  will be used.

  Args:
    y_true: Array of truth labels (must be same shape as y_pred).
    y_pred: Array of predicted labels (must be same shape as y_true).
    classes: Array of class labels (e.g. string form). If `None`, integer labels are used.
    figsize: Size of output figure (default=(10, 10)).
    text_size: Size of output figure text (default=15).
    norm: normalize values or not (default=False).
    savefig: save confusion matrix to file (default=False).
  
  Returns:
    A labelled confusion matrix plot comparing y_true and y_pred.

  Example usage:
    make_confusion_matrix(y_true=test_labels, # ground truth test labels
                          y_pred=y_preds, # predicted labels
                          classes=class_names, # array of class label names
                          figsize=(15, 15),
                          text_size=10)
  """  
  # Create the confustion matrix
  cm = confusion_matrix(y_true, y_pred)
  cm_norm = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis] # normalize it
  n_classes = cm.shape[0] # find the number of classes we're dealing with

  # Plot the figure and make it pretty
  fig, ax = plt.subplots(figsize=figsize)
  cax = ax.matshow(cm, cmap=plt.cm.Blues) # colors will represent how 'correct' a class is, darker == better
  fig.colorbar(cax)

  # Are there a list of classes?
  if classes:
    labels = classes
  else:
    labels = np.arange(cm.shape[0])
  
  # Label the axes
  ax.set(title="Confusion Matrix",
         xlabel="Predicted label",
         ylabel="True label",
         xticks=np.arange(n_classes), # create enough axis slots for each class
         yticks=np.arange(n_classes), 
         xticklabels=labels, # axes will labeled with class names (if they exist) or ints
         yticklabels=labels)
  
  # Make x-axis labels appear on bottom
  ax.xaxis.set_label_position("bottom")
  ax.xaxis.tick_bottom()
  
  plt.xticks(rotation=70, fontsize=text_size)
  plt.yticks(fontsize=text_size)

  # Set the threshold for different colors
  threshold = (cm.max() + cm.min()) / 2.

  # Plot the text on each cell
  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
    if norm:
      plt.text(j, i, f"{cm[i, j]} ({cm_norm[i, j]*100:.1f}%)",
              horizontalalignment="center",
              color="white" if cm[i, j] > threshold else "black",
              size=text_size)
    else:
      plt.text(j, i, f"{cm[i, j]}",
              horizontalalignment="center",
              color="white" if cm[i, j] > threshold else "black",
              size=text_size)

  # Save the figure to the current working directory
  if savefig:
    fig.savefig("confusion_matrix.png")
  
# Make a function to predict on images and plot them (works with multi-class)
def pred_and_plot(model, filename, class_names):
  """
  Imports an image located at filename, makes a prediction on it with
  a trained model and plots the image with the predicted class as the title.
  """
  # Import the target image and preprocess it
  img = load_and_prep_image(filename)

  # Make a prediction
  pred = model.predict(tf.expand_dims(img, axis=0))

  # Get the predicted class
  if len(pred[0]) > 1: # check for multi-class
    pred_class = class_names[pred.argmax()] # if more than one output, take the max
  else:
    pred_class = class_names[int(tf.round(pred)[0][0])] # if only one output, round

  # Plot the image and predicted class
  plt.imshow(img)
  plt.title(f"Prediction: {pred_class}")
  plt.axis(False);
  
import datetime

def create_tensorboard_callback(dir_name, experiment_name):
  """
  Creates a TensorBoard callback instand to store log files.

  Stores log files with the filepath:
    "dir_name/experiment_name/current_datetime/"

  Args:
    dir_name: target directory to store TensorBoard log files
    experiment_name: name of experiment directory (e.g. efficientnet_model_1)
  """
  log_dir = dir_name + "/" + experiment_name + "/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
  tensorboard_callback = tf.keras.callbacks.TensorBoard(
      log_dir=log_dir
  )
  print(f"Saving TensorBoard log files to: {log_dir}")
  return tensorboard_callback

# Plot the validation and training data separately
import matplotlib.pyplot as plt

def plot_loss_curves(history):
  """
  Returns separate loss curves for training and validation metrics.

  Args:
    history: TensorFlow model History object (see: https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/History)
  """ 
  loss = history.history['loss']
  val_loss = history.history['val_loss']

  accuracy = history.history['accuracy']
  val_accuracy = history.history['val_accuracy']

  epochs = range(len(history.history['loss']))

  # Plot loss
  plt.plot(epochs, loss, label='training_loss')
  plt.plot(epochs, val_loss, label='val_loss')
  plt.title('Loss')
  plt.xlabel('Epochs')
  plt.legend()

  # Plot accuracy
  plt.figure()
  plt.plot(epochs, accuracy, label='training_accuracy')
  plt.plot(epochs, val_accuracy, label='val_accuracy')
  plt.title('Accuracy')
  plt.xlabel('Epochs')
  plt.legend();

def compare_historys(original_history, new_history, initial_epochs=5):
    """
    Compares two TensorFlow model History objects.
    
    Args:
      original_history: History object from original model (before new_history)
      new_history: History object from continued model training (after original_history)
      initial_epochs: Number of epochs in original_history (new_history plot starts from here) 
    """
    
    # Get original history measurements
    acc = original_history.history["accuracy"]
    loss = original_history.history["loss"]

    val_acc = original_history.history["val_accuracy"]
    val_loss = original_history.history["val_loss"]

    # Combine original history with new history
    total_acc = acc + new_history.history["accuracy"]
    total_loss = loss + new_history.history["loss"]

    total_val_acc = val_acc + new_history.history["val_accuracy"]
    total_val_loss = val_loss + new_history.history["val_loss"]

    # Make plots
    plt.figure(figsize=(8, 8))
    plt.subplot(2, 1, 1)
    plt.plot(total_acc, label='Training Accuracy')
    plt.plot(total_val_acc, label='Validation Accuracy')
    plt.plot([initial_epochs-1, initial_epochs-1],
              plt.ylim(), label='Start Fine Tuning') # reshift plot around epochs
    plt.legend(loc='lower right')
    plt.title('Training and Validation Accuracy')

    plt.subplot(2, 1, 2)
    plt.plot(total_loss, label='Training Loss')
    plt.plot(total_val_loss, label='Validation Loss')
    plt.plot([initial_epochs-1, initial_epochs-1],
              plt.ylim(), label='Start Fine Tuning') # reshift plot around epochs
    plt.legend(loc='upper right')
    plt.title('Training and Validation Loss')
    plt.xlabel('epoch')
    plt.show()
  
# Create function to unzip a zipfile into current working directory 
# (since we're going to be downloading and unzipping a few files)
import zipfile

def unzip_data(filename):
  """
  Unzips filename into the current working directory.

  Args:
    filename (str): a filepath to a target zip folder to be unzipped.
  """
  zip_ref = zipfile.ZipFile(filename, "r")
  zip_ref.extractall()
  zip_ref.close()

# Walk through an image classification directory and find out how many files (images)
# are in each subdirectory.
import os

def walk_through_dir(dir_path):
  """
  Walks through dir_path returning its contents.

  Args:
    dir_path (str): target directory
  
  Returns:
    A print out of:
      number of subdiretories in dir_path
      number of images (files) in each subdirectory
      name of each subdirectory
  """
  for dirpath, dirnames, filenames in os.walk(dir_path):
    print(f"There are {len(dirnames)} directories and {len(filenames)} images in '{dirpath}'.")
    
# Function to evaluate: accuracy, precision, recall, f1-score
from sklearn.metrics import accuracy_score, precision_recall_fscore_support

def calculate_results(y_true, y_pred):
  """
  Calculates model accuracy, precision, recall and f1 score of a binary classification model.

  Args:
      y_true: true labels in the form of a 1D array
      y_pred: predicted labels in the form of a 1D array

  Returns a dictionary of accuracy, precision, recall, f1-score.
  """
  # Calculate model accuracy
  model_accuracy = accuracy_score(y_true, y_pred) * 100
  # Calculate model precision, recall and f1 score using "weighted average
  model_precision, model_recall, model_f1, _ = precision_recall_fscore_support(y_true, y_pred, average="weighted")
  model_results = {"accuracy": model_accuracy,
                  "precision": model_precision,
                  "recall": model_recall,
                  "f1": model_f1}
  return model_results

#Create Model Function to create a model from url
def create_model(model_url, num_classes = 10):
  '''
  Takes a TensorFlow Hub Url and creates a Keras Sequential Model wwith it
  
  Args: 
    model_url(str): A TensorFlow hub feature extraction url.
    num_classes(int): Number of out neurons, number of target classes default 10
  
  Returns: uncompiled model as extractor 

  '''
  feature_extractor_layer = hub.KerasLayer(model_url, 
                                           trainable = False,
                                           name ='feature_extraction_layer',
                                           input_shape = IMAGE_SHAPE +(3,))
  model = tf.keras.Sequential([feature_extractor_layer,layers.Dense(num_classes, activation ='softmax', name ='output_layer')])

  return model



import matplotlib.pyplot as plt
import matplotlib.image as mping
import os
import random
def view_argumented(target_clas, target_dir):

  '''
  target class should be the training data +.class_name and then the target_dir should be the directory of the training data

  '''
  target_class = random.choice(percent_train.class_names)
  target_dir ='/content/10_food_classes_1_percent/train/' + target_class
  random_image = random.choice(os.listdir(target_dir))
  random_image_path = target_dir + '/' +random_image
  #Read in 
  img = mping.imread(random_image_path)
  fin = plt.imshow(img)
  plt.title(f'Orginal Target Image from class {target_class} ')
  plt.axis(False)


#Plot Argumented
  augmented_img = data_augmentation(img, training =True)
  plt.figure()
  fin2 = plt.imshow(augmented_img/255.)
  plt.title(f'AugumentedImage')
  return fin, fin2

#Functionalise Model Checkpoint
import datetime

def create_model_checkpoint(file_name):
  '''
  Create Model Checkpoint Callback for any Model You are building

  Args:
  file_name: File_name will be the directory name which will have a timestamp to it.
  '''

  filepathdir = file_name + '/' +datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
  filepath = filepathdir + '.ckpt'
  checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath =filepath,
                                                         save_weight_only =True,
                                                         save_best_only=False,
                                                         save_freq ='epoch',
                                                         verbose =1)
  return checkpoint_callback

import tensorflow as tf
def load_and_prep_image(filename, img_shape=224, scale=True):
  """
  Reads in an image from filename, turns it into a tensor and reshapes into
  (224, 224, 3).

  Parameters
  ----------
  filename (str): string filename of target image
  img_shape (int): size to resize target image to, default 224
  scale (bool): whether to scale pixel values to range(0, 1), default True
  """
  # Read in the image
  img = tf.io.read_file(filename)
  # Decode it into a tensor
  img = tf.io.decode_image(img)
  # Resize the image
  img = tf.image.resize(img, [img_shape, img_shape])
  if scale:
    # Rescale the image (get all values between 0 and 1)
    return img/255.
  else:
    return img

  #Preprocess Images for Tensorflow ds
  def preprocess_img(image, label, img_shape= 224):
    '''
    Converts image datatypes from 'unit8 too float32 and reshapes image to 
    [img_shape, img_shape, color_channels]
  
    Args:
    image: image iterable to pass
    label: image label for tfds
    img_shape: image shape
    '''
    image =tf.image.resize(image, [img_shape, img_shape])
    #image = image/255. #uncomment to scale
    return tf.cast(image, tf.float32), label

import os 
def get_lines(filename):
  '''
  Reads Filename (a text file ) and returns the lines of text as a list

  Args:
  filename: a string containin the target filepath
  
  Returns: A list of strings with one string per line from the target filename
  '''
  with open(filename, 'r') as f:
    return f.readlines()
  
  #Splitting text into characters level

def split_chars(text):
  return ' '.join(list(text))



def lr_schedule(epoch):
    """Learning Rate Schedule
    Learning rate is scheduled to be reduced after 80, 120, 160, 180 epochs.
    Called automatically every epoch as part of callbacks during training.
    # Arguments
        epoch (int): The number of epochs
    # Returns
        lr (float32): learning rate
    """
    lr = 1e-3
    if epoch > 180:
        lr *= 0.5e-3
    elif epoch > 160:
        lr *= 1e-3
    elif epoch > 120:
        lr *= 1e-2
    elif epoch > 80:
        lr *= 1e-1
    print('Learning rate: ', lr)
    return lr