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Advanced Convolutional Neural Networks
Let's start by importing what we need and preparing as described in the comments
below:
import os
import time
import tensorflow as tf
import numpy as np
# How many categories we are predicting from (0-9)
LABEL_DIMENSIONS = 10
(train_images, train_labels), (test_images, test_labels) =
tf.keras.datasets.fashion_mnist.load_data()
TRAINING_SIZE = len(train_images)
TEST_SIZE = len(test_images)
train_images = np.asarray(train_images, dtype=np.float32) / 255
# Convert the train images and add channels
train_images = train_images.reshape((TRAINING_SIZE, 28, 28, 1))
test_images = np.asarray(test_images, dtype=np.float32) / 255
# Convert the train images and add channels
test_images = test_images.reshape((TEST_SIZE, 28, 28, 1))
train_labels = tf.keras.utils.to_categorical(train_labels, LABEL_
DIMENSIONS)
test_labels = tf.keras.utils.to_categorical(test_labels, LABEL_
DIMENSIONS)
# Cast the labels to float
train_labels = train_labels.astype(np.float32)
test_labels = test_labels.astype(np.float32)
print (train_labels.shape)
print (test_labels.shape)
Now let's build a convolutional model with the tf.Keras functional API:
inputs = tf.keras.Input(shape=(28,28,1))
x = tf.keras.layers.Conv2D(filters=32, kernel_size=(3, 3),
activation='relu')(inputs)
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=2)(x)
x = tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3),
activation='relu')(x)
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=2)(x)
x = tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3),
activation='relu')(x)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dense(64, activation='relu')(x)
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