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# -*- coding: utf-8 -*-
"""face-classification.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/#fileId=https%3A//huggingface.co/spaces/Tarive/Nepali_Actors_Prediction/blob/main/face-classification.ipynb
# Importing Libararies
"""
import os
import numpy as np # linear algebra
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import shutil
from PIL import Image
from sklearn.metrics import classification_report,confusion_matrix
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, load_img, img_to_array
from matplotlib.pyplot import imshow
from tensorflow.keras.callbacks import ReduceLROnPlateau
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras import Model
from tensorflow.keras import layers
"""# Looking into structure of file arrangements"""
DIR = '/kaggle/input/nepali-celeb-localized-face-dataset/Dataset/Dataset/'
files = os.listdir(DIR)
print(files)
class_count = len(files)
print(f'There are {class_count} classes.')
# Remove Non JPG images
for cls in files:
cls_path = os.path.join(DIR, cls)
imgs = os.listdir(cls_path)
img = Image.open(os.path.join(cls_path,imgs[0]))
print(f'Class {cls} contains {len(imgs)} images images of shape {img.size}.')
"""# Creating the data generator using ImageDataGenerator for the CNN"""
def train_val_generators():
"""
Creates the training and validation data generators
Returns:
train_generator, validation_generator: tuple containing the generators
"""
# Instantiate the ImageDataGenerator class, normalize pixel values and set arguments to augment the images
datagen = ImageDataGenerator(rescale=1.0/255.0,
rotation_range=40,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=True,
vertical_flip=True,
fill_mode='nearest',
validation_split=0.2)
# Pass in the appropriate arguments to the flow_from_directory method
train_generator = datagen.flow_from_directory(directory=DIR,
batch_size=100,
class_mode='categorical',
shuffle=True,
subset='training',
target_size=(75,75))
# Pass in the appropriate arguments to the flow_from_directory method
validation_generator = datagen.flow_from_directory(directory=DIR,
batch_size=36,
class_mode='categorical',
shuffle = False,
subset='validation',
target_size=(75, 75))
return train_generator, validation_generator
train_generator, validation_generator = train_val_generators()
"""# Define and compile the transfer learning model"""
pre_trained_model = tf.keras.applications.inception_v3.InceptionV3(
input_shape = (75, 75, 3),
include_top = False,
weights = 'imagenet')
for layer in pre_trained_model.layers:
layer.trainable = False
pre_trained_model.summary()
# Choose `mixed_7` as the last layer of your base model
last_layer = pre_trained_model.get_layer('mixed7')
print('last layer output shape: ', last_layer.output_shape)
last_output = last_layer.output
# Flatten the output layer to 1 dimension
x = layers.Flatten()(last_output)
# Add a fully connected layer with 1,024 hidden units and ReLU activation
x = layers.Dense(512, activation='relu')(x)
# Add a dropout rate of 0.2
x = layers.Dropout(0.2)(x)
# Add a final sigmoid layer for classification
x = layers.Dense (class_count, activation='softmax')(x)
# Append the dense network to the base model
model_transfer = Model(pre_trained_model.input, x)
# Print the model summary. See your dense network connected at the end.
model_transfer.summary()
model_transfer.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
"""# Creating a Callback class"""
class myCallback(tf.keras.callbacks.Callback):
# Define the correct function signature for on_epoch_end
def on_epoch_end(self, epoch, logs={}):
if (logs.get('val_accuracy') is not None and logs.get('val_accuracy') > 0.99):
print(logs.get('val_accuracy'))
print("\nReached 99% validation accuracy so cancelling training!")
callbacks = myCallback()
reduce_lr = ReduceLROnPlateau(
monitor='val_loss',
factor=0.25,
patience=2,
min_lr=0.00001,
verbose=2
)
checkpoint_path = "/kaggle/working/cp.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)
# Create a callback that saves the model's weights
cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
save_weights_only=True,
verbose=1)
"""# Train the model"""
history1 = model_transfer.fit(train_generator,
epochs=50,
validation_data=validation_generator,
callbacks=[callbacks, reduce_lr, cp_callback]
)
print("Accuracy of the transfer_learning model is - " , model_transfer.evaluate(validation_generator)[1]*100 , "%")
"""# Evaluating Accuracy and Loss for the Model"""
# Plot the chart for accuracy and loss on both training and validation
acc = history1.history['accuracy']
val_acc = history1.history['val_accuracy']
loss = history1.history['loss']
val_loss = history1.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'r', label='Training accuracy')
plt.plot(epochs, val_acc, 'b', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'r', label='Training Loss')
plt.plot(epochs, val_loss, 'b', label='Validation Loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
predictions = model_transfer.predict(validation_generator)
predictions=np.argmax(predictions,axis=-1)
print(predictions[:10])
print(validation_generator.labels[:10])
dict_cls = validation_generator.class_indices
list(dict_cls.keys())
"""# Evaluating Precision, Recall, F1-Score and Support for the Model"""
print(classification_report(validation_generator.labels, predictions, target_names = list(dict_cls.keys())))
"""# Plotting the Confusion Matrix for the Classification"""
cm = confusion_matrix(validation_generator.labels,predictions)
cm = pd.DataFrame(cm , index = list(dict_cls.keys()) , columns = list(dict_cls.keys()))
plt.figure(figsize = (15,15))
sns.heatmap(cm,cmap= "Blues", linecolor = 'black' , linewidth = 1 , annot = True, fmt='')
"""# Sample Model Prediction"""
def class_name(id):
key_list = list(dict_cls.keys())
val_list = list(dict_cls.values())
position = val_list.index(id)
return key_list[position]
f, ax = plt.subplots(10,3)
f.set_size_inches(10, 10)
k = 0
for i in range(10):
for j in range(3):
true_cls = validation_generator.labels[k]
true_cls = class_name(true_cls)
pred_cls = predictions[k]
pred_cls = class_name(pred_cls)
ax[i,j].set_title(f'Actual = {true_cls}\n Predicted = {pred_cls}')
img=plt.imread(DIR+validation_generator.filenames[k])
ax[i,j].imshow(img)
ax[i,j].axis('off')
k += 2
plt.tight_layout() |