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Retraining an Image Classifier

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Image classification models have millions of parameters. Training them from scratch requires a lot of labeled training data and a lot of computing power. Transfer learning is a technique that shortcuts much of this by taking a piece of a model that has already been trained on a related task and reusing it in a new model.

This Colab demonstrates how to build a Keras model for classifying five species of flowers by using a pre-trained TF2 SavedModel from TensorFlow Hub for image feature extraction, trained on the much larger and more general ImageNet dataset. Optionally, the feature extractor can be trained ("fine-tuned") alongside the newly added classifier.

Looking for a tool instead?

This is a TensorFlow coding tutorial. If you want a tool that just builds the TensorFlow or TF Lite model for, take a look at the make_image_classifier command-line tool that gets installed by the PIP package tensorflow-hub[make_image_classifier], or at this TF Lite colab.


import itertools
import os

import matplotlib.pylab as plt
import numpy as np

import tensorflow as tf
import tensorflow_hub as hub

print("TF version:", tf.__version__)
print("Hub version:", hub.__version__)
print("GPU is", "available" if tf.test.is_gpu_available() else "NOT AVAILABLE")
TF version: 2.4.1
Hub version: 0.11.0
WARNING:tensorflow:From <ipython-input-1-0831fa394ed3>:12: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.config.list_physical_devices('GPU')` instead.
GPU is available

Select the TF2 SavedModel module to use

For starters, use The same URL can be used in code to identify the SavedModel and in your browser to show its documentation. (Note that models in TF1 Hub format won't work here.)

You can find more TF2 models that generate image feature vectors here.

There are multiple possible models to try. All you need to do is select a different one on the cell below and follow up with the notebook.

model_name = "mobilenet_v3_small_100_224" # @param ['bit_s-r50x1', 'efficientnet_b0', 'efficientnet_b1', 'efficientnet_b2', 'efficientnet_b3', 'efficientnet_b4', 'efficientnet_b5', 'efficientnet_b6', 'efficientnet_b7', 'inception_v3', 'inception_resnet_v2', 'mobilenet_v2_100_224', 'mobilenet_v2_130_224', 'mobilenet_v2_140_224', 'mobilenet_v3_large_100_224', 'mobilenet_v3_large_075_224', 'mobilenet_v3_small_100_224', 'mobilenet_v3_small_075_224', 'nasnet_large', 'nasnet_mobile', 'pnasnet_large', 'resnet_v1_50', 'resnet_v1_101', 'resnet_v1_152', 'resnet_v2_50', 'resnet_v2_101', 'resnet_v2_152']

model_handle_map = {
  "efficientnet_b0": "",
  "efficientnet_b1": "",
  "efficientnet_b2": "",
  "efficientnet_b3": "",
  "efficientnet_b4": "",
  "efficientnet_b5": "",
  "efficientnet_b6": "",
  "efficientnet_b7": "",
  "bit_s-r50x1": "",
  "inception_v3": "",
  "inception_resnet_v2": "",
  "resnet_v1_50": "",
  "resnet_v1_101": "",
  "resnet_v1_152": "",
  "resnet_v2_50": "",
  "resnet_v2_101": "",
  "resnet_v2_152": "",
  "nasnet_large": "",
  "nasnet_mobile": "",
  "pnasnet_large": "",
  "mobilenet_v2_100_224": "",
  "mobilenet_v2_130_224": "",
  "mobilenet_v2_140_224": "",
  "mobilenet_v3_small_100_224": "",
  "mobilenet_v3_small_075_224": "",
  "mobilenet_v3_large_100_224": "",
  "mobilenet_v3_large_075_224": "",

model_image_size_map = {
  "efficientnet_b0": 224,
  "efficientnet_b1": 240,
  "efficientnet_b2": 260,
  "efficientnet_b3": 300,
  "efficientnet_b4": 380,
  "efficientnet_b5": 456,
  "efficientnet_b6": 528,
  "efficientnet_b7": 600,
  "inception_v3": 299,
  "inception_resnet_v2": 299,
  "nasnet_large": 331,
  "pnasnet_large": 331,

model_handle = model_handle_map.get(model_name)
pixels = model_image_size_map.get(model_name, 224)

print(f"Selected model: {model_name} : {model_handle}")

IMAGE_SIZE = (pixels, pixels)
print(f"Input size {IMAGE_SIZE}")

Selected model: mobilenet_v3_small_100_224 :
Input size (224, 224)

Set up the Flowers dataset

Inputs are suitably resized for the selected module. Dataset augmentation (i.e., random distortions of an image each time it is read) improves training, esp. when fine-tuning.

data_dir = tf.keras.utils.get_file(
Downloading data from
228818944/228813984 [==============================] - 14s 0us/step
datagen_kwargs = dict(rescale=1./255, validation_split=.20)
dataflow_kwargs = dict(target_size=IMAGE_SIZE, batch_size=BATCH_SIZE,

valid_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
valid_generator = valid_datagen.flow_from_directory(
    data_dir, subset="validation", shuffle=False, **dataflow_kwargs)

do_data_augmentation = False
if do_data_augmentation:
  train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
      width_shift_range=0.2, height_shift_range=0.2,
      shear_range=0.2, zoom_range=0.2,
  train_datagen = valid_datagen
train_generator = train_datagen.flow_from_directory(
    data_dir, subset="training", shuffle=True, **dataflow_kwargs)
Found 731 images belonging to 5 classes.
Found 2939 images belonging to 5 classes.

Defining the model

All it takes is to put a linear classifier on top of the feature_extractor_layer with the Hub module.

For speed, we start out with a non-trainable feature_extractor_layer, but you can also enable fine-tuning for greater accuracy.

do_fine_tuning = False
print("Building model with", model_handle)
model = tf.keras.Sequential([
    # Explicitly define the input shape so the model can be properly
    # loaded by the TFLiteConverter
    tf.keras.layers.InputLayer(input_shape=IMAGE_SIZE + (3,)),
    hub.KerasLayer(model_handle, trainable=do_fine_tuning),
Building model with
Model: "sequential"
Layer (type)                 Output Shape              Param #   
keras_layer (KerasLayer)     (None, 1024)              1529968   
dropout (Dropout)            (None, 1024)              0         
dense (Dense)                (None, 5)                 5125      
Total params: 1,535,093
Trainable params: 5,125
Non-trainable params: 1,529,968

Training the model

  optimizer=tf.keras.optimizers.SGD(lr=0.005, momentum=0.9), 
  loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True, label_smoothing=0.1),
steps_per_epoch = train_generator.samples // train_generator.batch_size
validation_steps = valid_generator.samples // valid_generator.batch_size
hist =
    epochs=5, steps_per_epoch=steps_per_epoch,
Epoch 1/5
91/91 [==============================] - 30s 174ms/step - loss: 1.1899 - accuracy: 0.5899 - val_loss: 0.7321 - val_accuracy: 0.8423
Epoch 2/5
91/91 [==============================] - 15s 160ms/step - loss: 0.6630 - accuracy: 0.8935 - val_loss: 0.7036 - val_accuracy: 0.8651
Epoch 3/5
91/91 [==============================] - 14s 158ms/step - loss: 0.6405 - accuracy: 0.9095 - val_loss: 0.6973 - val_accuracy: 0.8580
Epoch 4/5
91/91 [==============================] - 15s 160ms/step - loss: 0.6143 - accuracy: 0.9156 - val_loss: 0.6817 - val_accuracy: 0.8722
Epoch 5/5
91/91 [==============================] - 15s 160ms/step - loss: 0.5917 - accuracy: 0.9323 - val_loss: 0.6795 - val_accuracy: 0.8778
plt.ylabel("Loss (training and validation)")
plt.xlabel("Training Steps")

plt.ylabel("Accuracy (training and validation)")
plt.xlabel("Training Steps")
[<matplotlib.lines.Line2D at 0x7f0f2030b198>]



Try out the model on an image from the validation data:

def get_class_string_from_index(index):
   for class_string, class_index in valid_generator.class_indices.items():
      if class_index == index:
         return class_string

x, y = next(valid_generator)
image = x[0, :, :, :]
true_index = np.argmax(y[0])

# Expand the validation image to (1, 224, 224, 3) before predicting the label
prediction_scores = model.predict(np.expand_dims(image, axis=0))
predicted_index = np.argmax(prediction_scores)
print("True label: " + get_class_string_from_index(true_index))
print("Predicted label: " + get_class_string_from_index(predicted_index))


True label: daisy
Predicted label: daisy

Finally, the trained model can be saved for deployment to TF Serving or TF Lite (on mobile) as follows.

saved_model_path = f"/tmp/saved_flowers_model_{model_name}", saved_model_path)
INFO:tensorflow:Assets written to: /tmp/saved_flowers_model_mobilenet_v3_small_100_224/assets
INFO:tensorflow:Assets written to: /tmp/saved_flowers_model_mobilenet_v3_small_100_224/assets

Optional: Deployment to TensorFlow Lite

TensorFlow Lite lets you deploy TensorFlow models to mobile and IoT devices. The code below shows how to convert the trained model to TF Lite and apply post-training tools from the TensorFlow Model Optimization Toolkit. Finally, it runs it in the TF Lite Interpreter to examine the resulting quality

  • Converting without optimization provides the same results as before (up to roundoff error).
  • Converting with optimization without any data quantizes the model weights to 8 bits, but inference still uses floating-point computation for the neural network activations. This reduces model size almost by a factor of 4 and improves CPU latency on mobile devices.
  • On top, computation of the neural network activations can be quantized to 8-bit integers as well if a small reference dataset is provided to calibrate the quantization range. On a mobile device, this accelerates inference further and makes it possible to run on accelerators like EdgeTPU.

Optimization settings

Wrote TFLite model of 6097236 bytes.
interpreter = tf.lite.Interpreter(model_content=lite_model_content)
# This little helper wraps the TF Lite interpreter as a numpy-to-numpy function.
def lite_model(images):
  interpreter.set_tensor(interpreter.get_input_details()[0]['index'], images)
  return interpreter.get_tensor(interpreter.get_output_details()[0]['index'])
num_eval_examples = 50 
eval_dataset = ((image, label)  # TFLite expects batch size 1.
                for batch in train_generator
                for (image, label) in zip(*batch))
count = 0
count_lite_tf_agree = 0
count_lite_correct = 0
for image, label in eval_dataset:
  probs_lite = lite_model(image[None, ...])[0]
  probs_tf = model(image[None, ...]).numpy()[0]
  y_lite = np.argmax(probs_lite)
  y_tf = np.argmax(probs_tf)
  y_true = np.argmax(label)
  count +=1
  if y_lite == y_tf: count_lite_tf_agree += 1
  if y_lite == y_true: count_lite_correct += 1
  if count >= num_eval_examples: break
print("TF Lite model agrees with original model on %d of %d examples (%g%%)." %
      (count_lite_tf_agree, count, 100.0 * count_lite_tf_agree / count))
print("TF Lite model is accurate on %d of %d examples (%g%%)." %
      (count_lite_correct, count, 100.0 * count_lite_correct / count))
TF Lite model agrees with original model on 50 of 50 examples (100%).
TF Lite model is accurate on 47 of 50 examples (94%).