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aswintechguy
GitHub Repository: aswintechguy/Deep-Learning-Projects
 Path: blob/main/Dogs vs Cats Image Classification - CNN/Dogs vs Cats Image Classification - CNN.ipynb
569 views
Kernel: Python 3

Dataset Information

The training archive contains 25,000 images of dogs and cats. Train your algorithm on these files and predict the labels

(1 = dog, 0 = cat).

Download Dataset

!wget https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip
--2021-05-06 16:04:20-- https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip Resolving download.microsoft.com (download.microsoft.com)... 23.78.216.154, 2600:1417:8000:980::e59, 2600:1417:8000:9b2::e59 Connecting to download.microsoft.com (download.microsoft.com)|23.78.216.154|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 824894548 (787M) [application/octet-stream] Saving to: ‘kagglecatsanddogs_3367a.zip’ kagglecatsanddogs_3 100%[===================>] 786.68M 187MB/s in 4.3s 2021-05-06 16:04:24 (183 MB/s) - ‘kagglecatsanddogs_3367a.zip’ saved [824894548/824894548]

Unzip the Dataset

# !unzip kagglecatsanddogs_3367a.zip

Import Modules

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import warnings
import os
import tqdm
import random
from keras.preprocessing.image import load_img
warnings.filterwarnings('ignore')

Create Dataframe for Input and Output

input_path = []
label = []

for class_name in os.listdir("PetImages"):
    for path in os.listdir("PetImages/"+class_name):
        if class_name == 'Cat':
            label.append(0)
        else:
            label.append(1)
        input_path.append(os.path.join("PetImages", class_name, path))
print(input_path[0], label[0])
PetImages/Dog/4253.jpg 1 
df = pd.DataFrame()
df['images'] = input_path
df['label'] = label
df = df.sample(frac=1).reset_index(drop=True)
df.head()

for i in df['images']:
    if '.jpg' not in i:
        print(i)
PetImages/Cat/Thumbs.db PetImages/Dog/Thumbs.db 
import PIL
l = []
for image in df['images']:
    try:
        img = PIL.Image.open(image)
    except:
        l.append(image)
l
['PetImages/Cat/666.jpg', 'PetImages/Cat/Thumbs.db', 'PetImages/Dog/Thumbs.db', 'PetImages/Dog/11702.jpg']
# delete db files
df = df[df['images']!='PetImages/Dog/Thumbs.db']
df = df[df['images']!='PetImages/Cat/Thumbs.db']
df = df[df['images']!='PetImages/Cat/666.jpg']
df = df[df['images']!='PetImages/Dog/11702.jpg']
len(df)
24998

Exploratory Data Analysis

# to display grid of images
plt.figure(figsize=(25,25))
temp = df[df['label']==1]['images']
start = random.randint(0, len(temp))
files = temp[start:start+25]

for index, file in enumerate(files):
    plt.subplot(5,5, index+1)
    img = load_img(file)
    img = np.array(img)
    plt.imshow(img)
    plt.title('Dogs')
    plt.axis('off')
Image in a Jupyter notebook
# to display grid of images
plt.figure(figsize=(25,25))
temp = df[df['label']==0]['images']
start = random.randint(0, len(temp))
files = temp[start:start+25]

for index, file in enumerate(files):
    plt.subplot(5,5, index+1)
    img = load_img(file)
    img = np.array(img)
    plt.imshow(img)
    plt.title('Cats')
    plt.axis('off')
Image in a Jupyter notebook
import seaborn as sns
sns.countplot(df['label'])
<matplotlib.axes._subplots.AxesSubplot at 0x7f71e0c80190>
Image in a Jupyter notebook

Create DataGenerator for the Images

df['label'] = df['label'].astype('str')

df.head()

# input split
from sklearn.model_selection import train_test_split
train, test = train_test_split(df, test_size=0.2, random_state=42)

from keras.preprocessing.image import ImageDataGenerator
train_generator = ImageDataGenerator(
    rescale = 1./255,  # normalization of images
    rotation_range = 40, # augmention of images to avoid overfitting
    shear_range = 0.2,
    zoom_range = 0.2,
    horizontal_flip = True,
    fill_mode = 'nearest'
)

val_generator = ImageDataGenerator(rescale = 1./255)

train_iterator = train_generator.flow_from_dataframe(
    train, 
    x_col='images', 
    y_col='label', 
    target_size=(128,128), 
    batch_size=512, 
    class_mode='binary'
)

val_iterator = val_generator.flow_from_dataframe(
    test, 
    x_col='images', 
    y_col='label', 
    target_size=(128,128), 
    batch_size=512, 
    class_mode='binary'
)
Found 19998 validated image filenames belonging to 2 classes. Found 5000 validated image filenames belonging to 2 classes.

Model Creation

from keras import Sequential
from keras.layers import Conv2D, MaxPool2D, Flatten, Dense

model = Sequential([
                    Conv2D(16, (3,3), activation='relu', input_shape=(128,128,3)),
                    MaxPool2D((2,2)),
                    Conv2D(32, (3,3), activation='relu'),
                    MaxPool2D((2,2)),
                    Conv2D(64, (3,3), activation='relu'),
                    MaxPool2D((2,2)),
                    Flatten(),
                    Dense(512, activation='relu'),
                    Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.summary()
Model: "sequential_4" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_12 (Conv2D) (None, 126, 126, 16) 448 _________________________________________________________________ max_pooling2d_12 (MaxPooling (None, 63, 63, 16) 0 _________________________________________________________________ conv2d_13 (Conv2D) (None, 61, 61, 32) 4640 _________________________________________________________________ max_pooling2d_13 (MaxPooling (None, 30, 30, 32) 0 _________________________________________________________________ conv2d_14 (Conv2D) (None, 28, 28, 64) 18496 _________________________________________________________________ max_pooling2d_14 (MaxPooling (None, 14, 14, 64) 0 _________________________________________________________________ flatten_4 (Flatten) (None, 12544) 0 _________________________________________________________________ dense_8 (Dense) (None, 512) 6423040 _________________________________________________________________ dense_9 (Dense) (None, 1) 513 ================================================================= Total params: 6,447,137 Trainable params: 6,447,137 Non-trainable params: 0 _________________________________________________________________ 
history = model.fit(train_iterator, epochs=10, validation_data=val_iterator)
Epoch 1/10 40/40 [==============================] - 150s 4s/step - loss: 0.8679 - accuracy: 0.5187 - val_loss: 0.6399 - val_accuracy: 0.6238 Epoch 2/10 40/40 [==============================] - 147s 4s/step - loss: 0.6280 - accuracy: 0.6416 - val_loss: 0.5672 - val_accuracy: 0.7024 Epoch 3/10 40/40 [==============================] - 146s 4s/step - loss: 0.5737 - accuracy: 0.6980 - val_loss: 0.5493 - val_accuracy: 0.7148 Epoch 4/10 40/40 [==============================] - 146s 4s/step - loss: 0.5478 - accuracy: 0.7221 - val_loss: 0.5351 - val_accuracy: 0.7356 Epoch 5/10 40/40 [==============================] - 145s 4s/step - loss: 0.5276 - accuracy: 0.7338 - val_loss: 0.5104 - val_accuracy: 0.7494 Epoch 6/10 40/40 [==============================] - 144s 4s/step - loss: 0.5127 - accuracy: 0.7405 - val_loss: 0.4853 - val_accuracy: 0.7664 Epoch 7/10 40/40 [==============================] - 144s 4s/step - loss: 0.5059 - accuracy: 0.7544 - val_loss: 0.4586 - val_accuracy: 0.7868 Epoch 8/10 40/40 [==============================] - 143s 4s/step - loss: 0.4842 - accuracy: 0.7644 - val_loss: 0.5054 - val_accuracy: 0.7510 Epoch 9/10 40/40 [==============================] - 143s 4s/step - loss: 0.4971 - accuracy: 0.7530 - val_loss: 0.4647 - val_accuracy: 0.7894 Epoch 10/10 40/40 [==============================] - 142s 4s/step - loss: 0.4642 - accuracy: 0.7770 - val_loss: 0.4711 - val_accuracy: 0.7782

Visualization of Results

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
epochs = range(len(acc))

plt.plot(epochs, acc, 'b', label='Training Accuracy')
plt.plot(epochs, val_acc, 'r', label='Validation Accuracy')
plt.title('Accuracy Graph')
plt.legend()
plt.figure()

loss = history.history['loss']
val_loss = history.history['val_loss']
plt.plot(epochs, loss, 'b', label='Training Loss')
plt.plot(epochs, val_loss, 'r', label='Validation Loss')
plt.title('Loss Graph')
plt.legend()
plt.show()
Image in a Jupyter notebookImage in a Jupyter notebook

Test with Real Image

image_path = "test.jpg" # path of the image
img = load_img(image_path, target_size=(128, 128))
img = np.array(img)
img = img / 255.0 # normalize the image
img = img.reshape(1, 128, 128, 3) # reshape for prediction
pred = model.predict(img)
if pred[0] > 0.5:
    label = 'Dog'
else:
    label = 'Cat'
print(label)