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In this tutorial we will discuss how to classify cats vs dogs images by using transfer learning from a pre-trained network. This will allows us to get higher accuracies than we saw by training our network from scratch.
A **pre-trained model **is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. We can either use the pretrained model as it is or transfer learning using the pretrained convents. The intuition behind **transfer learning **is that if this model trained on a large and general enough dataset, this model will effectively serve as a generic model of the visual world. We can leverage these learned feature maps without having to train a large model on a large dataset by using these models as the basis of our own model specific to our task. There are 2 scenarios of transfer learning using a pretrained model:
- Feature Extraction - use the representations of learned by a previous network to extract meaningful features from new samples. We simply add a new classifier, which will be trained from scratch, on top of the pretrained model so that we can repurpose the feature maps learned previously for our dataset. Do we use the entire pretrained model or just the convolutional base? - We use the feature extraction portion of these pretrained convnets (convolutional base) since they are likely to be generic features and learned concepts over a picture. However, the classification part of the pretrained model is often specific to original classification task, and subsequently specific to the set of classes on which the model was trained.
- Fine-Tuning - unfreezing a few of the top layers of a frozen model base used for feature extraction, and jointly training both the newly added classifier layers as well as the last layers of the frozen model. This allows us to "fine tune" the higher order feature representations in addition to our final classifier in order to make them more relevant for the specific task involved.
We will follow the general machine learning workflow: 1. Examine and understand data 2. Build an input pipeline - using Keras ImageDataGenerator as we did in the image classification tutorial 3. Compose our model * Load in our pretrained model (and pretrained weights) * Stack our classification layers on top 4. Train our model 5. Evaluate model
We will see an example of using the pre-trained convnet as the feature extraction and then fine-tune to train the last few layers of the base model.
Audience: This post is geared towards beginners with some Keras API and ML background. To get the most out of this post, you should have some basic ML background, know what CNNs are, and be familiar with the Keras Sequential API.
Time Estimated: 30 minutes
from __future__ import absolute_import, division, print_function
import os
import tensorflow as tf
from tensorflow import keras
print("TensorFlow version is ", tf.__version__)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
TensorFlow version is 1.12.0
Data preprocessing
Download data - cats_and_dogs_filtered.zip
We will download a filtered version of Kaggle's Dogs vs Cats dataset. Then store the downloaded zip file to the "/tmp/" directory.
zip_file = tf.keras.utils.get_file(origin="https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip",
fname="cats_and_dogs_filtered.zip", extract=True)
base_dir, _ = os.path.splitext(zip_file)
Downloading data from https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip 68608000/68606236 [==============================] - 1s 0us/step
Prepare training and validation cats and dogs datasets
Create the training and validation directories for cats datasets and dog datasets.
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
# Directory with our training cat pictures
train_cats_dir = os.path.join(train_dir, 'cats')
print ('Total training cat images:', len(os.listdir(train_cats_dir)))
# Directory with our training dog pictures
train_dogs_dir = os.path.join(train_dir, 'dogs')
print ('Total training dog images:', len(os.listdir(train_dogs_dir)))
# Directory with our validation cat pictures
validation_cats_dir = os.path.join(validation_dir, 'cats')
print ('Total validation cat images:', len(os.listdir(validation_cats_dir)))
# Directory with our validation dog pictures
validation_dogs_dir = os.path.join(validation_dir, 'dogs')
print ('Total validation dog images:', len(os.listdir(validation_dogs_dir)))
Total training cat images: 1000 Total training dog images: 1000 Total validation cat images: 500 Total validation dog images: 500
Create Image Data Generator with Image Augmentation
We will use ImageDataGenerator to rescale the images.
Create the train generator and specify where the train dataset directory, image size, batch size and it's a binary classification.
Create the validation generator with similar approach as the train generator with the flow_from_directory() method.
image_size = 160 # All images will be resized to 160x160
batch_size = 32
# Rescale all images by 1./255 and apply image augmentation
train_datagen = keras.preprocessing.image.ImageDataGenerator(
rescale=1./255)
validation_datagen = keras.preprocessing.image.ImageDataGenerator(rescale=1./255)
# Flow training images in batches of 20 using train_datagen generator
train_generator = train_datagen.flow_from_directory(
train_dir, # Source directory for the training images
target_size=(image_size, image_size),
batch_size=batch_size,
# Since we use binary_crossentropy loss, we need binary labels
class_mode='binary')
# Flow validation images in batches of 20 using test_datagen generator
validation_generator = validation_datagen.flow_from_directory(
validation_dir, # Source directory for the validation images
target_size=(image_size, image_size),
batch_size=batch_size,
class_mode='binary')
Found 2000 images belonging to 2 classes. Found 1000 images belonging to 2 classes.
Create the base model from the pre-trained convnets
We will create the base model from the MobileNet V2 model developed at Google, and pre-trained on the ImageNet dataset, a large dataset of 1.4M images and 1000 classes of web images. This is a powerful model. Let's see what the features that it has learned can do for our cat vs. dog problem.
First, we need to pick which intermediate layer of MobileNet V2 we will use for feature extraction. A common practice is to use the output of the very last layer before the flatten operation, the so-called "bottleneck layer". The reasoning here is that the following fully-connected layers will be too specialized to the task the network was trained on, and thus the features learned by these layers won't be very useful for a new task. The bottleneck features, however, retain much generality.
Let's instantiate an MobileNet V2 model pre-loaded with weights trained on ImageNet. By specifying the include_top=False argument, we load a network that doesn't include the classification layers at the top, which is ideal for feature extraction.
IMG_SHAPE = (image_size, image_size, 3)
# Create the base model from the pre-trained model MobileNet V2
base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,
include_top=False,
weights='imagenet')
Downloading data from https://github.com/JonathanCMitchell/mobilenet_v2_keras/releases/download/v1.1/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_160_no_top.h5 9412608/9406464 [==============================] - 1s 0us/step
Feature extraction
We will freeze the convolutional base created from the previous step and use that as a feature extractor, add a classifier on top of it and train the top-level classifier.
Freeze the convolutional base
It's important to freeze the convolutional based before we compile and train the model. By freezing (or setting layer.trainable = False), we prevent the weights in these layers from being updated during training.
base_model.trainable = False
# Let's take a look at the base model architecture
base_model.summary()
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 160, 160, 3) 0
__________________________________________________________________________________________________
Conv1_pad (ZeroPadding2D) (None, 161, 161, 3) 0 input_1[0][0]
__________________________________________________________________________________________________
Conv1 (Conv2D) (None, 80, 80, 32) 864 Conv1_pad[0][0]
__________________________________________________________________________________________________
bn_Conv1 (BatchNormalization) (None, 80, 80, 32) 128 Conv1[0][0]
__________________________________________________________________________________________________
Conv1_relu (ReLU) (None, 80, 80, 32) 0 bn_Conv1[0][0]
__________________________________________________________________________________________________
expanded_conv_depthwise (Depthw (None, 80, 80, 32) 288 Conv1_relu[0][0]
__________________________________________________________________________________________________
expanded_conv_depthwise_BN (Bat (None, 80, 80, 32) 128 expanded_conv_depthwise[0][0]
__________________________________________________________________________________________________
expanded_conv_depthwise_relu (R (None, 80, 80, 32) 0 expanded_conv_depthwise_BN[0][0]
__________________________________________________________________________________________________
expanded_conv_project (Conv2D) (None, 80, 80, 16) 512 expanded_conv_depthwise_relu[0][0
__________________________________________________________________________________________________
expanded_conv_project_BN (Batch (None, 80, 80, 16) 64 expanded_conv_project[0][0]
__________________________________________________________________________________________________
block_1_expand (Conv2D) (None, 80, 80, 96) 1536 expanded_conv_project_BN[0][0]
__________________________________________________________________________________________________
block_1_expand_BN (BatchNormali (None, 80, 80, 96) 384 block_1_expand[0][0]
__________________________________________________________________________________________________
block_1_expand_relu (ReLU) (None, 80, 80, 96) 0 block_1_expand_BN[0][0]
__________________________________________________________________________________________________
block_1_pad (ZeroPadding2D) (None, 81, 81, 96) 0 block_1_expand_relu[0][0]
__________________________________________________________________________________________________
block_1_depthwise (DepthwiseCon (None, 40, 40, 96) 864 block_1_pad[0][0]
__________________________________________________________________________________________________
block_1_depthwise_BN (BatchNorm (None, 40, 40, 96) 384 block_1_depthwise[0][0]
__________________________________________________________________________________________________
block_1_depthwise_relu (ReLU) (None, 40, 40, 96) 0 block_1_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_1_project (Conv2D) (None, 40, 40, 24) 2304 block_1_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_1_project_BN (BatchNormal (None, 40, 40, 24) 96 block_1_project[0][0]
__________________________________________________________________________________________________
block_2_expand (Conv2D) (None, 40, 40, 144) 3456 block_1_project_BN[0][0]
__________________________________________________________________________________________________
block_2_expand_BN (BatchNormali (None, 40, 40, 144) 576 block_2_expand[0][0]
__________________________________________________________________________________________________
block_2_expand_relu (ReLU) (None, 40, 40, 144) 0 block_2_expand_BN[0][0]
__________________________________________________________________________________________________
block_2_depthwise (DepthwiseCon (None, 40, 40, 144) 1296 block_2_expand_relu[0][0]
__________________________________________________________________________________________________
block_2_depthwise_BN (BatchNorm (None, 40, 40, 144) 576 block_2_depthwise[0][0]
__________________________________________________________________________________________________
block_2_depthwise_relu (ReLU) (None, 40, 40, 144) 0 block_2_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_2_project (Conv2D) (None, 40, 40, 24) 3456 block_2_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_2_project_BN (BatchNormal (None, 40, 40, 24) 96 block_2_project[0][0]
__________________________________________________________________________________________________
block_2_add (Add) (None, 40, 40, 24) 0 block_1_project_BN[0][0]
block_2_project_BN[0][0]
__________________________________________________________________________________________________
block_3_expand (Conv2D) (None, 40, 40, 144) 3456 block_2_add[0][0]
__________________________________________________________________________________________________
block_3_expand_BN (BatchNormali (None, 40, 40, 144) 576 block_3_expand[0][0]
__________________________________________________________________________________________________
block_3_expand_relu (ReLU) (None, 40, 40, 144) 0 block_3_expand_BN[0][0]
__________________________________________________________________________________________________
block_3_pad (ZeroPadding2D) (None, 41, 41, 144) 0 block_3_expand_relu[0][0]
__________________________________________________________________________________________________
block_3_depthwise (DepthwiseCon (None, 20, 20, 144) 1296 block_3_pad[0][0]
__________________________________________________________________________________________________
block_3_depthwise_BN (BatchNorm (None, 20, 20, 144) 576 block_3_depthwise[0][0]
__________________________________________________________________________________________________
block_3_depthwise_relu (ReLU) (None, 20, 20, 144) 0 block_3_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_3_project (Conv2D) (None, 20, 20, 32) 4608 block_3_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_3_project_BN (BatchNormal (None, 20, 20, 32) 128 block_3_project[0][0]
__________________________________________________________________________________________________
block_4_expand (Conv2D) (None, 20, 20, 192) 6144 block_3_project_BN[0][0]
__________________________________________________________________________________________________
block_4_expand_BN (BatchNormali (None, 20, 20, 192) 768 block_4_expand[0][0]
__________________________________________________________________________________________________
block_4_expand_relu (ReLU) (None, 20, 20, 192) 0 block_4_expand_BN[0][0]
__________________________________________________________________________________________________
block_4_depthwise (DepthwiseCon (None, 20, 20, 192) 1728 block_4_expand_relu[0][0]
__________________________________________________________________________________________________
block_4_depthwise_BN (BatchNorm (None, 20, 20, 192) 768 block_4_depthwise[0][0]
__________________________________________________________________________________________________
block_4_depthwise_relu (ReLU) (None, 20, 20, 192) 0 block_4_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_4_project (Conv2D) (None, 20, 20, 32) 6144 block_4_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_4_project_BN (BatchNormal (None, 20, 20, 32) 128 block_4_project[0][0]
__________________________________________________________________________________________________
block_4_add (Add) (None, 20, 20, 32) 0 block_3_project_BN[0][0]
block_4_project_BN[0][0]
__________________________________________________________________________________________________
block_5_expand (Conv2D) (None, 20, 20, 192) 6144 block_4_add[0][0]
__________________________________________________________________________________________________
block_5_expand_BN (BatchNormali (None, 20, 20, 192) 768 block_5_expand[0][0]
__________________________________________________________________________________________________
block_5_expand_relu (ReLU) (None, 20, 20, 192) 0 block_5_expand_BN[0][0]
__________________________________________________________________________________________________
block_5_depthwise (DepthwiseCon (None, 20, 20, 192) 1728 block_5_expand_relu[0][0]
__________________________________________________________________________________________________
block_5_depthwise_BN (BatchNorm (None, 20, 20, 192) 768 block_5_depthwise[0][0]
__________________________________________________________________________________________________
block_5_depthwise_relu (ReLU) (None, 20, 20, 192) 0 block_5_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_5_project (Conv2D) (None, 20, 20, 32) 6144 block_5_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_5_project_BN (BatchNormal (None, 20, 20, 32) 128 block_5_project[0][0]
__________________________________________________________________________________________________
block_5_add (Add) (None, 20, 20, 32) 0 block_4_add[0][0]
block_5_project_BN[0][0]
__________________________________________________________________________________________________
block_6_expand (Conv2D) (None, 20, 20, 192) 6144 block_5_add[0][0]
__________________________________________________________________________________________________
block_6_expand_BN (BatchNormali (None, 20, 20, 192) 768 block_6_expand[0][0]
__________________________________________________________________________________________________
block_6_expand_relu (ReLU) (None, 20, 20, 192) 0 block_6_expand_BN[0][0]
__________________________________________________________________________________________________
block_6_pad (ZeroPadding2D) (None, 21, 21, 192) 0 block_6_expand_relu[0][0]
__________________________________________________________________________________________________
block_6_depthwise (DepthwiseCon (None, 10, 10, 192) 1728 block_6_pad[0][0]
__________________________________________________________________________________________________
block_6_depthwise_BN (BatchNorm (None, 10, 10, 192) 768 block_6_depthwise[0][0]
__________________________________________________________________________________________________
block_6_depthwise_relu (ReLU) (None, 10, 10, 192) 0 block_6_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_6_project (Conv2D) (None, 10, 10, 64) 12288 block_6_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_6_project_BN (BatchNormal (None, 10, 10, 64) 256 block_6_project[0][0]
__________________________________________________________________________________________________
block_7_expand (Conv2D) (None, 10, 10, 384) 24576 block_6_project_BN[0][0]
__________________________________________________________________________________________________
block_7_expand_BN (BatchNormali (None, 10, 10, 384) 1536 block_7_expand[0][0]
__________________________________________________________________________________________________
block_7_expand_relu (ReLU) (None, 10, 10, 384) 0 block_7_expand_BN[0][0]
__________________________________________________________________________________________________
block_7_depthwise (DepthwiseCon (None, 10, 10, 384) 3456 block_7_expand_relu[0][0]
__________________________________________________________________________________________________
block_7_depthwise_BN (BatchNorm (None, 10, 10, 384) 1536 block_7_depthwise[0][0]
__________________________________________________________________________________________________
block_7_depthwise_relu (ReLU) (None, 10, 10, 384) 0 block_7_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_7_project (Conv2D) (None, 10, 10, 64) 24576 block_7_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_7_project_BN (BatchNormal (None, 10, 10, 64) 256 block_7_project[0][0]
__________________________________________________________________________________________________
block_7_add (Add) (None, 10, 10, 64) 0 block_6_project_BN[0][0]
block_7_project_BN[0][0]
__________________________________________________________________________________________________
block_8_expand (Conv2D) (None, 10, 10, 384) 24576 block_7_add[0][0]
__________________________________________________________________________________________________
block_8_expand_BN (BatchNormali (None, 10, 10, 384) 1536 block_8_expand[0][0]
__________________________________________________________________________________________________
block_8_expand_relu (ReLU) (None, 10, 10, 384) 0 block_8_expand_BN[0][0]
__________________________________________________________________________________________________
block_8_depthwise (DepthwiseCon (None, 10, 10, 384) 3456 block_8_expand_relu[0][0]
__________________________________________________________________________________________________
block_8_depthwise_BN (BatchNorm (None, 10, 10, 384) 1536 block_8_depthwise[0][0]
__________________________________________________________________________________________________
block_8_depthwise_relu (ReLU) (None, 10, 10, 384) 0 block_8_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_8_project (Conv2D) (None, 10, 10, 64) 24576 block_8_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_8_project_BN (BatchNormal (None, 10, 10, 64) 256 block_8_project[0][0]
__________________________________________________________________________________________________
block_8_add (Add) (None, 10, 10, 64) 0 block_7_add[0][0]
block_8_project_BN[0][0]
__________________________________________________________________________________________________
block_9_expand (Conv2D) (None, 10, 10, 384) 24576 block_8_add[0][0]
__________________________________________________________________________________________________
block_9_expand_BN (BatchNormali (None, 10, 10, 384) 1536 block_9_expand[0][0]
__________________________________________________________________________________________________
block_9_expand_relu (ReLU) (None, 10, 10, 384) 0 block_9_expand_BN[0][0]
__________________________________________________________________________________________________
block_9_depthwise (DepthwiseCon (None, 10, 10, 384) 3456 block_9_expand_relu[0][0]
__________________________________________________________________________________________________
block_9_depthwise_BN (BatchNorm (None, 10, 10, 384) 1536 block_9_depthwise[0][0]
__________________________________________________________________________________________________
block_9_depthwise_relu (ReLU) (None, 10, 10, 384) 0 block_9_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_9_project (Conv2D) (None, 10, 10, 64) 24576 block_9_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_9_project_BN (BatchNormal (None, 10, 10, 64) 256 block_9_project[0][0]
__________________________________________________________________________________________________
block_9_add (Add) (None, 10, 10, 64) 0 block_8_add[0][0]
block_9_project_BN[0][0]
__________________________________________________________________________________________________
block_10_expand (Conv2D) (None, 10, 10, 384) 24576 block_9_add[0][0]
__________________________________________________________________________________________________
block_10_expand_BN (BatchNormal (None, 10, 10, 384) 1536 block_10_expand[0][0]
__________________________________________________________________________________________________
block_10_expand_relu (ReLU) (None, 10, 10, 384) 0 block_10_expand_BN[0][0]
__________________________________________________________________________________________________
block_10_depthwise (DepthwiseCo (None, 10, 10, 384) 3456 block_10_expand_relu[0][0]
__________________________________________________________________________________________________
block_10_depthwise_BN (BatchNor (None, 10, 10, 384) 1536 block_10_depthwise[0][0]
__________________________________________________________________________________________________
block_10_depthwise_relu (ReLU) (None, 10, 10, 384) 0 block_10_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_10_project (Conv2D) (None, 10, 10, 96) 36864 block_10_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_10_project_BN (BatchNorma (None, 10, 10, 96) 384 block_10_project[0][0]
__________________________________________________________________________________________________
block_11_expand (Conv2D) (None, 10, 10, 576) 55296 block_10_project_BN[0][0]
__________________________________________________________________________________________________
block_11_expand_BN (BatchNormal (None, 10, 10, 576) 2304 block_11_expand[0][0]
__________________________________________________________________________________________________
block_11_expand_relu (ReLU) (None, 10, 10, 576) 0 block_11_expand_BN[0][0]
__________________________________________________________________________________________________
block_11_depthwise (DepthwiseCo (None, 10, 10, 576) 5184 block_11_expand_relu[0][0]
__________________________________________________________________________________________________
block_11_depthwise_BN (BatchNor (None, 10, 10, 576) 2304 block_11_depthwise[0][0]
__________________________________________________________________________________________________
block_11_depthwise_relu (ReLU) (None, 10, 10, 576) 0 block_11_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_11_project (Conv2D) (None, 10, 10, 96) 55296 block_11_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_11_project_BN (BatchNorma (None, 10, 10, 96) 384 block_11_project[0][0]
__________________________________________________________________________________________________
block_11_add (Add) (None, 10, 10, 96) 0 block_10_project_BN[0][0]
block_11_project_BN[0][0]
__________________________________________________________________________________________________
block_12_expand (Conv2D) (None, 10, 10, 576) 55296 block_11_add[0][0]
__________________________________________________________________________________________________
block_12_expand_BN (BatchNormal (None, 10, 10, 576) 2304 block_12_expand[0][0]
__________________________________________________________________________________________________
block_12_expand_relu (ReLU) (None, 10, 10, 576) 0 block_12_expand_BN[0][0]
__________________________________________________________________________________________________
block_12_depthwise (DepthwiseCo (None, 10, 10, 576) 5184 block_12_expand_relu[0][0]
__________________________________________________________________________________________________
block_12_depthwise_BN (BatchNor (None, 10, 10, 576) 2304 block_12_depthwise[0][0]
__________________________________________________________________________________________________
block_12_depthwise_relu (ReLU) (None, 10, 10, 576) 0 block_12_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_12_project (Conv2D) (None, 10, 10, 96) 55296 block_12_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_12_project_BN (BatchNorma (None, 10, 10, 96) 384 block_12_project[0][0]
__________________________________________________________________________________________________
block_12_add (Add) (None, 10, 10, 96) 0 block_11_add[0][0]
block_12_project_BN[0][0]
__________________________________________________________________________________________________
block_13_expand (Conv2D) (None, 10, 10, 576) 55296 block_12_add[0][0]
__________________________________________________________________________________________________
block_13_expand_BN (BatchNormal (None, 10, 10, 576) 2304 block_13_expand[0][0]
__________________________________________________________________________________________________
block_13_expand_relu (ReLU) (None, 10, 10, 576) 0 block_13_expand_BN[0][0]
__________________________________________________________________________________________________
block_13_pad (ZeroPadding2D) (None, 11, 11, 576) 0 block_13_expand_relu[0][0]
__________________________________________________________________________________________________
block_13_depthwise (DepthwiseCo (None, 5, 5, 576) 5184 block_13_pad[0][0]
__________________________________________________________________________________________________
block_13_depthwise_BN (BatchNor (None, 5, 5, 576) 2304 block_13_depthwise[0][0]
__________________________________________________________________________________________________
block_13_depthwise_relu (ReLU) (None, 5, 5, 576) 0 block_13_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_13_project (Conv2D) (None, 5, 5, 160) 92160 block_13_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_13_project_BN (BatchNorma (None, 5, 5, 160) 640 block_13_project[0][0]
__________________________________________________________________________________________________
block_14_expand (Conv2D) (None, 5, 5, 960) 153600 block_13_project_BN[0][0]
__________________________________________________________________________________________________
block_14_expand_BN (BatchNormal (None, 5, 5, 960) 3840 block_14_expand[0][0]
__________________________________________________________________________________________________
block_14_expand_relu (ReLU) (None, 5, 5, 960) 0 block_14_expand_BN[0][0]
__________________________________________________________________________________________________
block_14_depthwise (DepthwiseCo (None, 5, 5, 960) 8640 block_14_expand_relu[0][0]
__________________________________________________________________________________________________
block_14_depthwise_BN (BatchNor (None, 5, 5, 960) 3840 block_14_depthwise[0][0]
__________________________________________________________________________________________________
block_14_depthwise_relu (ReLU) (None, 5, 5, 960) 0 block_14_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_14_project (Conv2D) (None, 5, 5, 160) 153600 block_14_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_14_project_BN (BatchNorma (None, 5, 5, 160) 640 block_14_project[0][0]
__________________________________________________________________________________________________
block_14_add (Add) (None, 5, 5, 160) 0 block_13_project_BN[0][0]
block_14_project_BN[0][0]
__________________________________________________________________________________________________
block_15_expand (Conv2D) (None, 5, 5, 960) 153600 block_14_add[0][0]
__________________________________________________________________________________________________
block_15_expand_BN (BatchNormal (None, 5, 5, 960) 3840 block_15_expand[0][0]
__________________________________________________________________________________________________
block_15_expand_relu (ReLU) (None, 5, 5, 960) 0 block_15_expand_BN[0][0]
__________________________________________________________________________________________________
block_15_depthwise (DepthwiseCo (None, 5, 5, 960) 8640 block_15_expand_relu[0][0]
__________________________________________________________________________________________________
block_15_depthwise_BN (BatchNor (None, 5, 5, 960) 3840 block_15_depthwise[0][0]
__________________________________________________________________________________________________
block_15_depthwise_relu (ReLU) (None, 5, 5, 960) 0 block_15_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_15_project (Conv2D) (None, 5, 5, 160) 153600 block_15_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_15_project_BN (BatchNorma (None, 5, 5, 160) 640 block_15_project[0][0]
__________________________________________________________________________________________________
block_15_add (Add) (None, 5, 5, 160) 0 block_14_add[0][0]
block_15_project_BN[0][0]
__________________________________________________________________________________________________
block_16_expand (Conv2D) (None, 5, 5, 960) 153600 block_15_add[0][0]
__________________________________________________________________________________________________
block_16_expand_BN (BatchNormal (None, 5, 5, 960) 3840 block_16_expand[0][0]
__________________________________________________________________________________________________
block_16_expand_relu (ReLU) (None, 5, 5, 960) 0 block_16_expand_BN[0][0]
__________________________________________________________________________________________________
block_16_depthwise (DepthwiseCo (None, 5, 5, 960) 8640 block_16_expand_relu[0][0]
__________________________________________________________________________________________________
block_16_depthwise_BN (BatchNor (None, 5, 5, 960) 3840 block_16_depthwise[0][0]
__________________________________________________________________________________________________
block_16_depthwise_relu (ReLU) (None, 5, 5, 960) 0 block_16_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_16_project (Conv2D) (None, 5, 5, 320) 307200 block_16_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_16_project_BN (BatchNorma (None, 5, 5, 320) 1280 block_16_project[0][0]
__________________________________________________________________________________________________
Conv_1 (Conv2D) (None, 5, 5, 1280) 409600 block_16_project_BN[0][0]
__________________________________________________________________________________________________
Conv_1_bn (BatchNormalization) (None, 5, 5, 1280) 5120 Conv_1[0][0]
__________________________________________________________________________________________________
out_relu (ReLU) (None, 5, 5, 1280) 0 Conv_1_bn[0][0]
==================================================================================================
Total params: 2,257,984
Trainable params: 0
Non-trainable params: 2,257,984
__________________________________________________________________________________________________
Add a classification head
Now let's add a few layers on top of the base model:
model = tf.keras.Sequential([
base_model,
keras.layers.GlobalAveragePooling2D(),
keras.layers.Dense(1, activation='sigmoid')
])
Compile the model
You must compile the model before training it.
model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=0.0001),
loss='binary_crossentropy',
metrics=['accuracy'])
model.summary()
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= mobilenetv2_1.00_160 (Model) (None, 5, 5, 1280) 2257984 _________________________________________________________________ global_average_pooling2d (Gl (None, 1280) 0 _________________________________________________________________ dense (Dense) (None, 1) 1281 ================================================================= Total params: 2,259,265 Trainable params: 1,281 Non-trainable params: 2,257,984 _________________________________________________________________
These 1.2K trainable parameters are divided among 2 TensorFlow Variable objects, the weights and biases of the two dense layers:
len(model.trainable_variables)
2
Train the model
After training for 10 epochs, we are able to get ~94% accuracy.
If you have more time, train it to convergence (50 epochs, ~96% accuracy)
epochs = 10
steps_per_epoch = train_generator.n // batch_size
validation_steps = validation_generator.n // batch_size
history = model.fit_generator(train_generator,
steps_per_epoch = steps_per_epoch,
epochs=epochs,
workers=4,
validation_data=validation_generator,
validation_steps=validation_steps)
Epoch 1/10 62/62 [==============================] - 36s 574ms/step - loss: 0.6543 - acc: 0.6220 - val_loss: 0.5704 - val_acc: 0.6966 Epoch 2/10 62/62 [==============================] - 33s 525ms/step - loss: 0.5294 - acc: 0.7444 - val_loss: 0.4263 - val_acc: 0.8367 Epoch 3/10 62/62 [==============================] - 32s 521ms/step - loss: 0.4411 - acc: 0.8236 - val_loss: 0.3428 - val_acc: 0.8810 Epoch 4/10 62/62 [==============================] - 33s 536ms/step - loss: 0.3795 - acc: 0.8584 - val_loss: 0.2877 - val_acc: 0.9062 Epoch 5/10 62/62 [==============================] - 33s 536ms/step - loss: 0.3421 - acc: 0.8619 - val_loss: 0.2536 - val_acc: 0.9204 Epoch 6/10 62/62 [==============================] - 32s 524ms/step - loss: 0.3217 - acc: 0.8795 - val_loss: 0.2364 - val_acc: 0.9204 Epoch 7/10 62/62 [==============================] - 33s 530ms/step - loss: 0.2796 - acc: 0.8982 - val_loss: 0.2116 - val_acc: 0.9325 Epoch 8/10 62/62 [==============================] - 33s 529ms/step - loss: 0.2677 - acc: 0.9002 - val_loss: 0.2030 - val_acc: 0.9304 Epoch 9/10 62/62 [==============================] - 33s 532ms/step - loss: 0.2546 - acc: 0.9057 - val_loss: 0.1882 - val_acc: 0.9375 Epoch 10/10 62/62 [==============================] - 32s 524ms/step - loss: 0.2306 - acc: 0.9168 - val_loss: 0.1828 - val_acc: 0.9395
Learning curves
Let's take a look at the learning curves of the training and validation accuracy / loss, when using the MobileNet V2 base model as a fixed feature extractor.
If you train to convergence (epochs=50) the resulting graph should look like this:
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.ylabel('Accuracy')
plt.ylim([min(plt.ylim()),1])
plt.title('Training and Validation Accuracy')
plt.subplot(2, 1, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.ylabel('Cross Entropy')
plt.ylim([0,max(plt.ylim())])
plt.title('Training and Validation Loss')
plt.show()
Fine tuning
In our feature extraction experiment, we were only training a few layers on top of an MobileNet V2 base model. The weights of the pre-trained network were not updated during training. One way to increase performance even further is to "fine-tune" the weights of the top layers of the pre-trained model alongside the training of the top-level classifier. The training process will force the weights to be tuned from generic features maps to features associated specifically to our dataset.
Additionally, the reasoning behind fine-tuning the top layers of the pre-trained model rather than all layers of the pre-trained model is the following: in a convnet, the higher up a layer is, the more specialized it is. The first few layers in a convnet learned very simple and generic features, which generalize to almost all types of images. But as you go higher up, the features are increasingly more specific to the dataset that the model was trained on. The goal of fine-tuning is to adapt these specialized features to work with the new dataset.
Un-freeze the top layers of the model
All we need to do is unfreeze the base_model, and set the bottom layers be un-trainable. Then, recompile the model (necessary for these changes to take effect), and resume training.
base_model.trainable = True
# Let's take a look to see how many layers are in the base model
print("Number of layers in the base model: ", len(base_model.layers))
# Fine tune from this layer onwards
fine_tune_at = 100
# Freeze all the layers before the `fine_tune_at` layer
for layer in base_model.layers[:fine_tune_at]:
layer.trainable = False
Number of layers in the base model: 155
Compile the model
Compile the model using a much-lower training rate.
model.compile(loss='binary_crossentropy',
optimizer = tf.keras.optimizers.RMSprop(lr=2e-5),
metrics=['accuracy'])
model.summary()
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= mobilenetv2_1.00_160 (Model) (None, 5, 5, 1280) 2257984 _________________________________________________________________ global_average_pooling2d (Gl (None, 1280) 0 _________________________________________________________________ dense (Dense) (None, 1) 1281 ================================================================= Total params: 2,259,265 Trainable params: 1,863,873 Non-trainable params: 395,392 _________________________________________________________________
len(model.trainable_variables)
58
Continue Train the model
If you trained to convergence earlier, this will get you a few percent more accuracy.
history_fine = model.fit_generator(train_generator,
steps_per_epoch = steps_per_epoch,
epochs=epochs,
workers=4,
validation_data=validation_generator,
validation_steps=validation_steps)
Epoch 1/10 62/62 [==============================] - 51s 822ms/step - loss: 0.1639 - acc: 0.9380 - val_loss: 0.1005 - val_acc: 0.9667 Epoch 2/10 62/62 [==============================] - 51s 828ms/step - loss: 0.0958 - acc: 0.9693 - val_loss: 0.0870 - val_acc: 0.9677 Epoch 3/10 62/62 [==============================] - 49s 789ms/step - loss: 0.0587 - acc: 0.9839 - val_loss: 0.0815 - val_acc: 0.9677 Epoch 4/10 62/62 [==============================] - 50s 805ms/step - loss: 0.0425 - acc: 0.9894 - val_loss: 0.0815 - val_acc: 0.9708 Epoch 5/10 62/62 [==============================] - 48s 775ms/step - loss: 0.0255 - acc: 0.9955 - val_loss: 0.0772 - val_acc: 0.9748 Epoch 6/10 62/62 [==============================] - 48s 773ms/step - loss: 0.0204 - acc: 0.9970 - val_loss: 0.0757 - val_acc: 0.9778 Epoch 7/10 62/62 [==============================] - 47s 765ms/step - loss: 0.0182 - acc: 0.9955 - val_loss: 0.0743 - val_acc: 0.9798 Epoch 8/10 62/62 [==============================] - 48s 771ms/step - loss: 0.0092 - acc: 0.9990 - val_loss: 0.0717 - val_acc: 0.9788 Epoch 9/10 62/62 [==============================] - 49s 792ms/step - loss: 0.0112 - acc: 0.9985 - val_loss: 0.0770 - val_acc: 0.9788 Epoch 10/10 62/62 [==============================] - 48s 773ms/step - loss: 0.0096 - acc: 0.9980 - val_loss: 0.0800 - val_acc: 0.9768
Learning curves
Let's take a look at the learning curves of the training and validation accuracy / loss, when fine tuning the last few layers of the MobileNet V2 base model, as well as the classifier on top of it. Note the validation loss much higher than the training loss which means there maybe some overfitting.
Note: the training dataset is fairly small, and is similar to the original datasets that MobileNet V2 was trained on, so fine-tuning may result in overfitting.
If you train to convergence (epochs=50) the resulting graph should look like this:
acc += history_fine.history['acc']
val_acc += history_fine.history['val_acc']
loss += history_fine.history['loss']
val_loss += history_fine.history['val_loss']
plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.ylim([0.9, 1])
plt.plot([epochs-1,epochs-1], plt.ylim(), label='Start Fine Tuning')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(2, 1, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.ylim([0, 0.2])
plt.plot([epochs-1,epochs-1], plt.ylim(), label='Start Fine Tuning')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
Key takeaways
In summary here is what we covered in this tutorial on how to do transfer learning using a pre-trained model to improve accuracy: * Using a pre-trained model for feature extraction - when working with a small dataset, it is common to leverage the features learned by a model trained on a larger dataset in the same domain. This is done by instantiating the pre-trained model and adding a fully connected classifier on top. The pre-trained model is "frozen" and only the weights of the classifier are updated during training. In this case, the convolutional base extracts all the features associated with each image and we train a classifier that determines, given these set of features to which class it belongs. * Fine-tuning a pre-trained model - to further improve performance, one might want to repurpose the top-level layers of the pre-trained models to the new dataset via fine-tuning. In this case, we tune our weights such that we learn highly specified and high level features specific to our dataset. This only make sense when the training dataset is large and very similar to the orginial dataset that the pre-trained model was trained on.
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