Deep Learning Blog

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Experiment 1

        
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Salary_Data.csv')
X = dataset.iloc[:, :-1].values  # Features (Years of Experience)
y = dataset.iloc[:, -1].values   # Target (Salary)

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/3, random_state=0)

# Training the Simple Linear Regression model on the Training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

# Predicting the Test set results
y_pred = regressor.predict(X_test)

# Visualizing the Training set results
plt.scatter(X_train, y_train, color='red')
plt.plot(X_train, regressor.predict(X_train), color='blue')
plt.title('Salary vs Experience (Training set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()

# Visualizing the Test set results
plt.scatter(X_test, y_test, color='red')
plt.plot(X_train, regressor.predict(X_train), color='blue')
plt.title('Salary vs Experience (Test set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()    
    

Experiment 2

Experiment 3

        
        

import matplotlib.pyplot as plt
import tensorflow as tf
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings('ignore')

from tensorflow import keras
from keras import layers
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.utils import image_dataset_from_directory
from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img
from tensorflow.keras.preprocessing import image_dataset_from_directory

import os
import matplotlib.image as mpimg
from zipfile import ZipFile

data_path = 'dog-vs-cat-classification.zip'

with ZipFile(data_path, 'r') as zip:
    zip.extractall()
    print('The data set has been extracted.')

path = 'dog-vs-cat-classification'
classes = os.listdir(path)
print(classes)

fig = plt.gcf()
fig.set_size_inches(16, 16)

cat_dir = os.path.join('dog-vs-cat-classification/cats')
dog_dir = os.path.join('dog-vs-cat-classification/dogs')
cat_names = os.listdir(cat_dir)
dog_names = os.listdir(dog_dir)

pic_index = 210

cat_images = [os.path.join(cat_dir, fname) for fname in cat_names[pic_index-8:pic_index]]
dog_images = [os.path.join(dog_dir, fname) for fname in dog_names[pic_index-8:pic_index]]

for i, img_path in enumerate(cat_images + dog_images):
    sp = plt.subplot(4, 4, i+1)
    sp.axis('Off')
    img = mpimg.imread(img_path)
    plt.imshow(img)

plt.show()

base_dir = 'dog-vs-cat-classification'

# Create datasets
train_datagen = image_dataset_from_directory(base_dir,
                                             image_size=(200, 200),
                                             subset='training',
                                             seed=1,
                                             validation_split=0.1,
                                             batch_size=32)
test_datagen = image_dataset_from_directory(base_dir,
                                            image_size=(200, 200),
                                            subset='validation',
                                            seed=1,
                                            validation_split=0.1,
                                            batch_size=32)

model = tf.keras.models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(200, 200, 3)),
    layers.MaxPooling2D(2, 2),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D(2, 2),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D(2, 2),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D(2, 2),

    layers.Flatten(),
    layers.Dense(512, activation='relu'),
    layers.BatchNormalization(),
    layers.Dense(512, activation='relu'),
    layers.Dropout(0.1),
    layers.BatchNormalization(),
    layers.Dense(512, activation='relu'),
    layers.Dropout(0.2),
    layers.BatchNormalization(),
    layers.Dense(1, activation='sigmoid')
])
model.summary()

keras.utils.plot_model(
    model,
    show_shapes=True,
    show_dtype=True,
    show_layer_activations=True
)

model.compile(
    loss='binary_crossentropy',
    optimizer='adam',
    metrics=['accuracy']
)

history = model.fit(train_datagen,
                    epochs=10,
                    validation_data=test_datagen)

history_df = pd.DataFrame(history.history)
history_df.loc[:, ['loss', 'val_loss']].plot()
history_df.loc[:, ['accuracy', 'val_accuracy']].plot()
plt.show()

from keras.preprocessing import image

# Input image
test_image_path = 'dog-vs-cat-classification/test/1.jpg'
test_image = load_img(test_image_path, target_size=(200, 200))

# Show image
plt.imshow(test_image)
plt.show()

test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis=0)

# Result array
result = model.predict(test_image)

# Mapping result array with the main name list
if result >= 0.5:
    print("Dog")
else:
    print("Cat")

# Another test image
test_image_path = 'dog-vs-cat-classification/test/2.jpg'
test_image = load_img(test_image_path, target_size=(200, 200))

# Show image
plt.imshow(test_image)
plt.show()

test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis=0)

# Result array
result = model.predict(test_image)

# Mapping result array with the main name list
if result >= 0.5:
    print("Dog")
else:
    print("Cat")
    

csv file 3

Experiment Exp 4

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Experiment 5

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Experiment 6

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Experiment 7

csv file 7

Experiment 8

csv file 8

Experiment 9

csv file 9