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functions.py
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functions.py
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# -*- coding: utf-8 -*-
"""helper_functions.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1NVca7aU5lIJv2l9LPoG-zd1RnnSFhgyf
"""
import tensorflow as tf
# function to unzip file into current working directory
import zipfile
def unzip_data(filename):
"""
Args:
filename (str) = a filepath to a target zip folder to be unzipped
"""
zip_file = zipfile.ZipFile(filename, "r")
zip_file.extractall()
zip_file.close()
# function to explore image classification directory
import os
def walk_through_dir(dir_path):
"""
Walk through dir_path returning its contents:
number of subdirectories,
name of each directory,
number of files (images) in each directory
"""
for dirpath, dirnames, filenames in os.walk(dir_path):
print(f"There are {len(dirnames)} directories and {len(filenames)} images in '{dirpath}'")
# function to import an image and resize it to be able to fit in the model
def load_prep_image(filename,img_shape=224, scale=True):
"""
Read an image from filename, turn it into a tensor, and reshape it to (img_shape, img_shape, color_channel)
--
Parameters:
filename (str) : filename of target image
img_shape (int) : size of target image to be resized
scale (boolean) : whether to scale pixal values of image to range(0,1), default=True
"""
# read image from the target file
img = tf.io.read_file(filename)
# decode image into tensor
img = tf.image.decode_jpeg(img) # color_channels=3 by default in decode_jpeg
# resize the image to the same size as the model has been trained on
img = tf.image.resize(img, size=[img_shape, img_shape])
# rescale the image
if scale:
return img/255.
else:
return img
# function to plot loss and accuracy curves
import matplotlib.pyplot as plt
def plot_loss_accuracy(history):
"""
Return loss and accuracy plot separately
Args:
history = tensorflow model 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='train_loss')
plt.plot(epochs, val_loss, label='val_loss')
plt.title('Loss Curves')
plt.xlabel('Epochs')
plt.legend()
# plot accuracy
plt.figure()
plt.plot(epochs, accuracy, label='train_accuracy')
plt.plot(epochs, val_accuracy, label='val_accuracy')
plt.title('Accuracy Curves')
plt.xlabel('Epochs')
plt.legend()
# function to compare history of the model
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()
# function to create confusion matrix
import itertools
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import confusion_matrix
def create_confusion_matrix(y_true, y_pred, classes=None, figsize=(10,10), text_size=15, norm=False, savefig=False):
"""
Create a labelled confusion matrix, comparing prediction and actual labels.
Args:
y_true = array of actual labels
y_pred = array of predicted labels (must be same as y_true in shape)
classes = array of class labels. 'None' : matrix will use integer as labels
figsize = size of confusion matrix figure
text_size = size of figure text (default = 15)
norm = whether to normalize values (default=False)
Returns
A labelled confusion matrix plot
Example Usage:
create_confusion_matrix(y_true=test_labels,
y_pred=y_preds,
classes=class_names,
figsize=(15,15),
test_size=12)
"""
# create confusion matrix
cm = confusion_matrix(y_true, y_pred)
cm_norm = cm.astype("float")/cm.sum(axis=1)[:, np.newaxis] # normalizing
n_classes = cm.shape[0] # number of classes
# plot the figure
fig, ax = plt.subplots(figsize=figsize)
cax = ax.matshow(cm, cmap=plt.cm.Blues)
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 Labek",
xticks=np.arange(n_classes),
yticks=np.arange(n_classes),
xticklabels=labels,
yticklabels=labels)
# Make x-axis label appears on bottom of figure
ax.xaxis.set_label_position("bottom")
ax.xaxis.tick_bottom()
# rotate xticks for readability
plt.xticks(rotation=70, fontsize=text_size)
plt.yticks(fontsize=text_size)
# Set the threshold for different colors
threshold = (cm.max() + cm.min())/2
# set up the cell text
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 "navy",
size=text_size)
else:
plt.text(j, i, f"{cm[i, j]}",
horizontalalignment="center",
color="white" if cm[i,j] > threshold else "navy",
size=text_size)
# save the figure to the current working directory
if savefig:
fig.savefig("Confusion_matrix.png")
# function to evaluate model prediction(accuracy, precision, recall, f1-score)
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
def evaluate_prediction(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_evaluation = {"accuracy": model_accuracy,
"precision": model_precision,
"recall": model_recall,
"f1": model_f1}
return model_evaluation
# function to predict images class and plot them (multiclass case)
def pred_view(model, filename, class_names):
"""
Import an image from filename, predict the class name with a trained model
and plot the image with the predicted class name
"""
# import the image and preprocess it
img = load_prep_image(filename)
# make a prediction
pred = model.predict(tf.expand_dims(img, axis=0))
# get the predicted class name
if len(pred[0]) > 1: # checking for multiclass classification
pred_class = class_names[pred.argmax()] # the max value is the class
else:
pred_class = class_names[int(tf.round(pred)[0][0])] # for binary classification
# plot the image and predicted class name
plt.imshow(img)
plt.title(f"Prediction: {pred_class}")
plt.axis(False)
# function to create tensorboard callback
import datetime
def create_tensorboard_callback(dir_name, experiment_name):
"""
Store 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
"""
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
# Function to compare 2 model's performances
def compare_baseline_to_new_model(baseline_evaluation, new_model_evaluation):
for key, value in baseline_evaluation.items():
print(f"Baseline {key}: {value:.2f}, New_model {key}: {new_model_evaluation[key]:.2f}, Difference: {new_model_evaluation[key]-value:.2f}")
return model_evaluation