Mac Mini M1 (Test) - Basic classification: Classify images of clothing

Sun, 03 Oct 2021 07:56:23 +0000

{loadmoduleid 116}

Implementation

Fashion-MNIST is a dataset consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 pixel grayscale image, associated with a label from 10 classes.

In [118]:

# TensorFlow and tf.keras
import tensorflow as tf

# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
from os import getcwd

print('\u2022 Using TensorFlow Version:', tf.__version__)
print('\u2022 GPU Device Found.' if tf.test.is_gpu_available() else '\u2022 GPU Device Not Found. Running on CPU')

• Using TensorFlow Version: 2.6.0
• GPU Device Found.

2021-10-31 14:59:28.709594: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2021-10-31 14:59:28.711011: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: )

Import the Fashion MNIST training and test datasets into four NumPy arrays. Each image is 28 x 28 pixels.

In [119]:

fashion_mnist = tf.keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

The images are 28x28 NumPy arrays, with pixel values ranging from 0 to 255. The labels are an array of integers, ranging from 0 to 9. These correspond to the class of clothing the image represents:

Label	Class
0	T-shirt/top
1	Trouser
2	Pullover
3	Dress
4	Coat
5	Sandal
6	Shirt
7	Sneaker
8	Bag
9	Ankle boot

Each image is mapped to a single label. Since the class names are not included with the dataset, store them here to use later when plotting the images:

In [120]:

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

In [121]:

train_images.shape

Out[121]:

(60000, 28, 28)

In [122]:

len(train_labels)

Out[122]:

In [123]:

train_labels

Out[123]:

array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)

In [124]:

len(test_labels)

Out[124]:

In [125]:

test_images.shape

Out[125]:

(10000, 28, 28)

In [126]:

plt.figure()
plt.imshow(train_images[1])
plt.colorbar()
plt.grid(False)
plt.show()

The images have greyscale values, between 0 and 255. Hence scale the values, between 0 and 1.

In [127]:

train_images = train_images / 255.0

test_images = test_images / 255.0

Show a couple of images.

In [128]:

plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i]])
plt.show()

Build the Model

The first layer in this network, tf.keras.layers.Flatten, transforms the format of the images from a two-dimensional array (of 28 by 28 pixels) to a one-dimensional array (of 28 * 28 = 784 pixels). After the pixels are flattened, the network consists of a sequence of two tf.keras.layers.Dense layers. These are densely connected, or fully connected, neural layers. The first Dense layer has 128 nodes (or neurons). The second (and last) layer returns a logits array with length of 10. Each node contains a score that indicates the current image belongs to one of the 10 classes.

In [129]:

model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10)
])

Compile the model

Before the model is ready for training, it needs a few more settings. These are added during the model's compile step:

Loss function —This measures how accurate the model is during training. You want to minimize this function to "steer" the model in the right direction. Optimizer — This is how the model is updated based on the data it sees and its loss function. Metrics — Used to monitor the training and testing steps. The following example uses accuracy, the fraction of the images that are correctly classified.

In [130]:

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

Train the Model

To start training, call the model.fit method—so called because it "fits" the model to the training data:

In [131]:

model.fit(train_images, train_labels, epochs=10)

Epoch 1/10
  35/1875 [..............................] - ETA: 5s - loss: 1.3832 - accuracy: 0.5491

2021-10-31 14:59:30.405955: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.

1875/1875 [==============================] - 6s 3ms/step - loss: 0.5015 - accuracy: 0.8231
Epoch 2/10
1875/1875 [==============================] - 5s 3ms/step - loss: 0.3748 - accuracy: 0.8649
Epoch 3/10
1875/1875 [==============================] - 5s 3ms/step - loss: 0.3356 - accuracy: 0.8772
Epoch 4/10
1875/1875 [==============================] - 5s 3ms/step - loss: 0.3115 - accuracy: 0.8838
Epoch 5/10
1875/1875 [==============================] - 5s 3ms/step - loss: 0.2916 - accuracy: 0.8931
Epoch 6/10
1875/1875 [==============================] - 5s 3ms/step - loss: 0.2789 - accuracy: 0.8951
Epoch 7/10
1875/1875 [==============================] - 5s 3ms/step - loss: 0.2649 - accuracy: 0.9004
Epoch 8/10
1875/1875 [==============================] - 5s 3ms/step - loss: 0.2524 - accuracy: 0.9061
Epoch 9/10
1875/1875 [==============================] - 5s 3ms/step - loss: 0.2446 - accuracy: 0.9092
Epoch 10/10
1875/1875 [==============================] - 5s 3ms/step - loss: 0.2372 - accuracy: 0.9108

Out[131]:

Evaluate accuracy

Next, compare how the model performs on the test dataset:

In [132]:

test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)

print('\nTest accuracy:', test_acc)

2021-10-31 15:00:24.988052: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.

313/313 - 1s - loss: 0.3345 - accuracy: 0.8804

Test accuracy: 0.8804000616073608

Make predictions

With the model trained, you can use it to make predictions about some images. The model's linear outputs, logits. Attach a softmax layer to convert the logits to probabilities, which are easier to interpret.

In [133]:

probability_model = tf.keras.Sequential([model, 
                                         tf.keras.layers.Softmax()])

In [134]:

predictions = probability_model.predict(test_images)

2021-10-31 15:00:25.763975: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.

In [135]:

predictions[0]

Out[135]:

array([3.3962785e-08, 5.2536460e-12, 4.5928550e-10, 1.3885787e-11,
       7.6916924e-11, 2.2161782e-02, 6.3888947e-08, 3.9380090e-03,
       3.7821670e-09, 9.7390002e-01], dtype=float32)

Graph the predictions to look at the full set of 10 class predictions.

In [136]:

def plot_image(i, predictions_array, true_label, img):
  true_label, img = true_label[i], img[i]
  plt.grid(False)
  plt.xticks([])
  plt.yticks([])

  plt.imshow(img, cmap=plt.cm.binary)

  predicted_label = np.argmax(predictions_array)
  if predicted_label == true_label:
    color = 'blue'
  else:
    color = 'red'

  plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
                                100*np.max(predictions_array),
                                class_names[true_label]),
                                color=color)

def plot_value_array(i, predictions_array, true_label):
  true_label = true_label[i]
  plt.grid(False)
  plt.xticks(range(10))
  plt.yticks([])
  thisplot = plt.bar(range(10), predictions_array, color="#777777")
  plt.ylim([0, 1])
  predicted_label = np.argmax(predictions_array)

  thisplot[predicted_label].set_color('red')
  thisplot[true_label].set_color('blue')

In [137]:

i = 0
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions[i], test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions[i],  test_labels)
plt.show()

In [138]:

i = 12
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions[i], test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions[i],  test_labels)
plt.show()

In [139]:

# Plot the first X test images, their predicted labels, and the true labels.
# Color correct predictions in blue and incorrect predictions in red.
num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
  plt.subplot(num_rows, 2*num_cols, 2*i+1)
  plot_image(i, predictions[i], test_labels, test_images)
  plt.subplot(num_rows, 2*num_cols, 2*i+2)
  plot_value_array(i, predictions[i], test_labels)
plt.tight_layout()
plt.show()

In [140]:

# Grab an image from the test dataset.
img = test_images[1]

print(img.shape)

(28, 28)

In [141]:

# Add the image to a batch where it's the only member.
img = (np.expand_dims(img,0))

print(img.shape)

(1, 28, 28)

In [142]:

predictions_single = probability_model.predict(img)

print(predictions_single)

[[4.2759009e-05 8.0097751e-12 9.9578261e-01 3.2288022e-10 3.3681127e-03
  3.4885767e-14 8.0647180e-04 3.3935744e-18 7.4961015e-10 1.2566573e-13]]

In [143]:

plot_value_array(1, predictions_single[0], test_labels)
_ = plt.xticks(range(10), class_names, rotation=45)
plt.show()

In [144]:

np.argmax(predictions_single[0])

Out[144]:

In [145]:

# MIT License
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# Copyright (c) 2017 François Chollet
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In [ ]:

GIMP (2.10) - Overlaying Grid Lines

Sun, 26 Sep 2021 11:45:18 +0000

Recently, in order to articulate a point in an article, I had to superimposed a grid on some photographs. I decided to use the GNU Image Manipulation Program or GIMP (2.10), with the ultimate aim of using it as a replacement to Adobe Photoshop. After all, its an open source package so cheap, as in free. Although, I plan to donate to the program, if and when I can. This blog post provides a quick overview of overlaying Grids in GIMP.

How do I?