An image classifier pipeline using conv and pooling

fashion-mnist-clf-conv-pool

import tensorflow as tf

# Initiating the class
mnist = tf.keras.datasets.fashion_mnist

# Downloading the data
(training_images, training_labels), (test_images, test_labels) = mnist.load_data()

# Reshaping the images for the convolutional input
training_images=training_images.reshape(60000, 28, 28, 1)
test_images = test_images.reshape(10000, 28, 28, 1)

# Normalizing the images
training_images=training_images / 255.0
test_images=test_images/255.0

# Adding the conv layer and max pooling layer
model = tf.keras.models.Sequential([
    # Convolutional layer
    tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(28, 28, 1)),
    
    # Max pooling layer
    tf.keras.layers.MaxPooling2D(2, 2),

    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='sigmoid')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Getting the summary of the model
model.summary()

# Fiting the model 
# Fitting the model
model.fit(training_images, training_labels, epochs=10)

# Evaluating accuracy
test_loss = model.evaluate(test_images, test_labels, verbose=False)
print(test_loss[1])