In [0]:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
This notebook uses the Fashion MNIST dataset which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels), as seen here:
|
|
|
Figure 1. Fashion-MNIST samples (by Zalando, MIT License). |
Fashion MNIST is intended as a drop-in replacement for the classic MNIST dataset—often used as the "Hello, World" of machine learning programs for computer vision. The MNIST dataset contains images of handwritten digits (0, 1, 2, etc.) in a format identical to that of the articles of clothing we'll use here.
This uses Fashion MNIST for variety, and because it's a slightly more challenging problem than regular MNIST. Both datasets are relatively small and are used to verify that an algorithm works as expected. They're good starting points to test and debug code.
We will use 60,000 images to train the network and 10,000 images to evaluate how accurately the network learned to classify images. You can access the Fashion MNIST directly from TensorFlow. Import and load the Fashion MNIST data directly from TensorFlow:
In [0]:
# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras
import tensorflow_datasets as tfds
tfds.disable_progress_bar()
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
import pathlib
print(tf.__version__)
In [0]:
splits = tfds.Split.ALL.subsplit(weighted=(80, 10, 10))
splits, info = tfds.load('fashion_mnist', with_info=True, as_supervised=True, split=splits)
(train_examples, validation_examples, test_examples) = splits
num_examples = info.splits['train'].num_examples
num_classes = info.features['label'].num_classes
In [0]:
class_names = ['T-shirt_top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
In [0]:
with open('labels.txt', 'w') as f:
f.write('\n'.join(class_names))
In [0]:
IMG_SIZE = 28
In [0]:
# Write a function to normalize and resize the images
def format_example(image, label):
# Cast image to float32
image = # YOUR CODE HERE
# Resize the image if necessary
image = # YOUR CODE HERE
# Normalize the image in the range [0, 1]
image = # YOUR CODE HERE
return image, label
In [0]:
# Set the batch size to 32
BATCH_SIZE = 32
In [0]:
# Prepare the examples by preprocessing the them and then batching them (and optionally prefetching them)
# If you wish you can shuffle train set here
train_batches = # YOUR CODE HERE
validation_batches = # YOUR CODE HERE
test_batches = # YOUR CODE HERE
In [0]:
"""
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 26, 26, 16) 160
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 13, 13, 16) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 11, 11, 32) 4640
_________________________________________________________________
flatten (Flatten) (None, 3872) 0
_________________________________________________________________
dense (Dense) (None, 64) 247872
_________________________________________________________________
dense_1 (Dense) (None, 10) 650
=================================================================
Total params: 253,322
Trainable params: 253,322
Non-trainable params: 0
"""
In [0]:
# Build the model shown in the previous cell
model = tf.keras.Sequential([
# Set the input shape to (28, 28, 1), kernel size=3, filters=16 and use ReLU activation,
tf.keras.layers.Conv2D(# YOUR CODE HERE),
tf.keras.layers.MaxPooling2D(),
# Set the number of filters to 32, kernel size to 3 and use ReLU activation
tf.keras.layers.Conv2D(# YOUR CODE HERE),
# Flatten the output layer to 1 dimension
tf.keras.layers.Flatten(),
# Add a fully connected layer with 64 hidden units and ReLU activation
tf.keras.layers.Dense(# YOUR CODE HERE),
# Attach a final softmax classification head
tf.keras.layers.Dense(# YOUR CODE HERE)])
# Set the loss and accuracy metrics
model.compile(
optimizer='adam',
loss=# YOUR CODE HERE,
metrics=# YOUR CODE HERE)
In [0]:
model.fit(train_batches,
epochs=10,
validation_data=validation_batches)
In [0]:
export_dir = 'saved_model/1'
# Use the tf.saved_model API to export the SavedModel
# Your Code Here
In [0]:
#@title Select mode of optimization
mode = "Speed" #@param ["Default", "Storage", "Speed"]
if mode == 'Storage':
optimization = tf.lite.Optimize.OPTIMIZE_FOR_SIZE
elif mode == 'Speed':
optimization = tf.lite.Optimize.OPTIMIZE_FOR_LATENCY
else:
optimization = tf.lite.Optimize.DEFAULT
In [0]:
optimization
In [0]:
# Use the TFLiteConverter SavedModel API to initialize the converter
converter = # YOUR CODE HERE
# Set the optimzations
converter.optimizations = # YOUR CODE HERE
# Invoke the converter to finally generate the TFLite model
tflite_model = # YOUR CODE HERE
In [0]:
tflite_model_file = 'model.tflite'
with open(tflite_model_file, "wb") as f:
f.write(tflite_model)
In [0]:
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
input_index = interpreter.get_input_details()[0]["index"]
output_index = interpreter.get_output_details()[0]["index"]
In [0]:
# Gather results for the randomly sampled test images
predictions = []
test_labels = []
test_images = []
for img, label in test_batches.take(50):
interpreter.set_tensor(input_index, img)
interpreter.invoke()
predictions.append(interpreter.get_tensor(output_index))
test_labels.append(label[0])
test_images.append(np.array(img))
In [0]:
#@title Utility functions for plotting
# Utilities for plotting
def plot_image(i, predictions_array, true_label, img):
predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
img = np.squeeze(img)
plt.imshow(img, cmap=plt.cm.binary)
predicted_label = np.argmax(predictions_array)
if predicted_label == true_label.numpy():
color = 'green'
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):
predictions_array, true_label = predictions_array[i], true_label[i]
plt.grid(False)
plt.xticks(list(range(10)), class_names, rotation='vertical')
plt.yticks([])
thisplot = plt.bar(range(10), predictions_array[0], color="#777777")
plt.ylim([0, 1])
predicted_label = np.argmax(predictions_array[0])
thisplot[predicted_label].set_color('red')
thisplot[true_label].set_color('green')
In [0]:
#@title Visualize the outputs { run: "auto" }
index = 49 #@param {type:"slider", min:1, max:50, step:1}
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(index, predictions, test_labels, test_images)
plt.show()
plot_value_array(index, predictions, test_labels)
plt.show()
In [0]:
try:
from google.colab import files
files.download(tflite_model_file)
files.download('labels.txt')
except:
pass
Now once you've the trained TFLite model downloaded, you can ahead and deploy this on an Android/iOS application by placing the model assets in the appropriate location.
In [0]:
!mkdir -p test_images
In [0]:
from PIL import Image
for index, (image, label) in enumerate(test_batches.take(50)):
image = tf.cast(image * 255.0, tf.uint8)
image = tf.squeeze(image).numpy()
pil_image = Image.fromarray(image)
pil_image.save('test_images/{}_{}.jpg'.format(class_names[label[0]].lower(), index))
In [0]:
!ls test_images
In [0]:
!zip -qq fmnist_test_images.zip -r test_images/
In [0]:
try:
files.download('fmnist_test_images.zip')
except:
pass