cat&dog.py

# -*- coding: utf-8 -*-
"""Cat&Dogs.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/17AFfKN67SFvxF7FdjjugeJGIa000SGU8
"""

import tensorflow as tf

#load the datasets
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()

#pre-process the data 
x_train = tf.keras.utils.normalize(x_train, axis=1) 
x_test = tf.keras.utils.normalize(x_test, axis=1)

#define the model input and set the layers
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation=tf.nn.leaky_relu))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.leaky_relu))
model.add(tf.keras.layers.Dense(10, activation=tf.nn.sigmoid))

#compile the model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

#train the model
model.fit(x_train, y_train, epochs=100)

#evaluate the model
val_loss, val_acc = model.evaluate(x_test, y_test)
print(val_loss)
print(val_acc)

#make predictions
predictions = model.predict(x_test)

import matplotlib.pyplot as plt
import numpy as np

# Select 5 random images from the test set
indices = np.random.randint(0, len(x_test), size=1)
images = x_test[indices]

# Make predictions for the selected images
predictions = model.predict(images)

# Iterate over the images and predictions
for i, (image, prediction) in enumerate(zip(images, predictions)):
  # Convert the image to uint8 and reshape it to (32, 32, 3)
  image = np.uint8(image * 255).reshape(32, 32, 3)

  # Get the class label and probability
  label = np.argmax(prediction)
  probability = prediction[label]

  # Plot the image and the prediction
  plt.subplot(1, 5, i + 1)
  plt.imshow(image)

  
# The labels of the CIFAR-10 dataset are represented as integers in the range 0 to 9. Each integer corresponds to a class of image:

# 0: airplane
# 1: automobile
# 2: bird
# 3: cat
# 4: deer
# 5: dog
# 6: frog
# 7: horse
# 8: ship
# 9: truck

  plt.title("Prediction: {} ({:.2f})".format(label, probability))

plt.show()