Keras documentation: Image classification via fine-tuning with EfficientNet (2024)

Author: Yixing Fu
Date created: 2020/06/30
Last modified: 2023/07/10
Description: Use EfficientNet with weights pre-trained on imagenet for Stanford Dogs classification.

ⓘ This example uses Keras 3

View in Colab • GitHub source

Introduction: what is EfficientNet

EfficientNet, first introduced in Tan and Le, 2019is among the most efficient models (i.e. requiring least FLOPS for inference)that reaches State-of-the-Art accuracy on bothimagenet and common image classification transfer learning tasks.

The smallest base model is similar to MnasNet, whichreached near-SOTA with a significantly smaller model. By introducing a heuristic way toscale the model, EfficientNet provides a family of models (B0 to B7) that represents agood combination of efficiency and accuracy on a variety of scales. Such a scalingheuristics (compound-scaling, details seeTan and Le, 2019) allows theefficiency-oriented base model (B0) to surpass models at every scale, while avoidingextensive grid-search of hyperparameters.

A summary of the latest updates on the model is available athere, where variousaugmentation schemes and semi-supervised learning approaches are applied to furtherimprove the imagenet performance of the models. These extensions of the model can be usedby updating weights without changing model architecture.

B0 to B7 variants of EfficientNet

(This section provides some details on "compound scaling", and can be skippedif you're only interested in using the models)

Based on the original paper people may have theimpression that EfficientNet is a continuous family of models created by arbitrarilychoosing scaling factor in as Eq.(3) of the paper. However, choice of resolution,depth and width are also restricted by many factors:

• Resolution: Resolutions not divisible by 8, 16, etc. cause zero-padding near boundariesof some layers which wastes computational resources. This especially applies to smallervariants of the model, hence the input resolution for B0 and B1 are chosen as 224 and240.

• Depth and width: The building blocks of EfficientNet demands channel size to bemultiples of 8.

• Resource limit: Memory limitation may bottleneck resolution when depthand width can still increase. In such a situation, increasing depth and/orwidth but keep resolution can still improve performance.

As a result, the depth, width and resolution of each variant of the EfficientNet modelsare hand-picked and proven to produce good results, though they may be significantlyoff from the compound scaling formula.Therefore, the keras implementation (detailed below) only provide these 8 models, B0 to B7,instead of allowing arbitray choice of width / depth / resolution parameters.

Keras implementation of EfficientNet

An implementation of EfficientNet B0 to B7 has been shipped with Keras since v2.3. Touse EfficientNetB0 for classifying 1000 classes of images from ImageNet, run:

from tensorflow.keras.applications import EfficientNetB0model = EfficientNetB0(weights='imagenet')

This model takes input images of shape (224, 224, 3), and the input data should be in therange [0, 255]. Normalization is included as part of the model.

Because training EfficientNet on ImageNet takes a tremendous amount of resources andseveral techniques that are not a part of the model architecture itself. Hence the Kerasimplementation by default loads pre-trained weights obtained via training withAutoAugment.

For B0 to B7 base models, the input shapes are different. Here is a list of input shapeexpected for each model:

When the model is intended for transfer learning, the Keras implementationprovides a option to remove the top layers:

model = EfficientNetB0(include_top=False, weights='imagenet')

This option excludes the final Dense layer that turns 1280 features on the penultimatelayer into prediction of the 1000 ImageNet classes. Replacing the top layer with customlayers allows using EfficientNet as a feature extractor in a transfer learning workflow.

Another argument in the model constructor worth noticing is drop_connect_rate which controlsthe dropout rate responsible for stochastic depth.This parameter serves as a toggle for extra regularization in finetuning, but does notaffect loaded weights. For example, when stronger regularization is desired, try:

model = EfficientNetB0(weights='imagenet', drop_connect_rate=0.4)

The default value is 0.2.

Example: EfficientNetB0 for Stanford Dogs.

EfficientNet is capable of a wide range of image classification tasks.This makes it a good model for transfer learning.As an end-to-end example, we will show using pre-trained EfficientNetB0 onStanford Dogs dataset.

Setup and data loading

import numpy as npimport tensorflow_datasets as tfdsimport tensorflow as tf # For tf.dataimport matplotlib.pyplot as pltimport kerasfrom keras import layersfrom keras.applications import EfficientNetB0# IMG_SIZE is determined by EfficientNet model choiceIMG_SIZE = 224BATCH_SIZE = 64

Loading data

Here we load data from tensorflow_datasets(hereafter TFDS).Stanford Dogs dataset is provided inTFDS as stanford_dogs.It features 20,580 images that belong to 120 classes of dog breeds(12,000 for training and 8,580 for testing).

By simply changing dataset_name below, you may also try this notebook forother datasets in TFDS such ascifar10,cifar100,food101,etc. When the images are much smaller than the size of EfficientNet input,we can simply upsample the input images. It has been shown inTan and Le, 2019 that transfer learningresult is better for increased resolution even if input images remain small.

dataset_name = "stanford_dogs"(ds_train, ds_test), ds_info = tfds.load( dataset_name, split=["train", "test"], with_info=True, as_supervised=True)NUM_CLASSES = ds_info.features["label"].num_classes

When the dataset include images with various size, we need to resize them into ashared size. The Stanford Dogs dataset includes only images at least 200x200pixels in size. Here we resize the images to the input size needed for EfficientNet.

size = (IMG_SIZE, IMG_SIZE)ds_train = ds_train.map(lambda image, label: (tf.image.resize(image, size), label))ds_test = ds_test.map(lambda image, label: (tf.image.resize(image, size), label))

Visualizing the data

The following code shows the first 9 images with their labels.

def format_label(label): string_label = label_info.int2str(label) return string_label.split("-")[1]label_info = ds_info.features["label"]for i, (image, label) in enumerate(ds_train.take(9)): ax = plt.subplot(3, 3, i + 1) plt.imshow(image.numpy().astype("uint8")) plt.title("{}".format(format_label(label))) plt.axis("off")

Data augmentation

We can use the preprocessing layers APIs for image augmentation.

img_augmentation_layers = [ layers.RandomRotation(factor=0.15), layers.RandomTranslation(height_factor=0.1, width_factor=0.1), layers.RandomFlip(), layers.RandomContrast(factor=0.1),]def img_augmentation(images): for layer in img_augmentation_layers: images = layer(images) return images

This Sequential model object can be used both as a part ofthe model we later build, and as a function to preprocessdata before feeding into the model. Using them as function makesit easy to visualize the augmented images. Here we plot 9 examplesof augmentation result of a given figure.

for image, label in ds_train.take(1): for i in range(9): ax = plt.subplot(3, 3, i + 1) aug_img = img_augmentation(np.expand_dims(image.numpy(), axis=0)) aug_img = np.array(aug_img) plt.imshow(aug_img[0].astype("uint8")) plt.title("{}".format(format_label(label))) plt.axis("off")

Prepare inputs

Once we verify the input data and augmentation are working correctly,we prepare dataset for training. The input data are resized to uniformIMG_SIZE. The labels are put into one-hot(a.k.a. categorical) encoding. The dataset is batched.

Note: prefetch and AUTOTUNE may in some situation improveperformance, but depends on environment and the specific dataset used.See this guidefor more information on data pipeline performance.

# One-hot / categorical encodingdef input_preprocess_train(image, label): image = img_augmentation(image) label = tf.one_hot(label, NUM_CLASSES) return image, labeldef input_preprocess_test(image, label): label = tf.one_hot(label, NUM_CLASSES) return image, labelds_train = ds_train.map(input_preprocess_train, num_parallel_calls=tf.data.AUTOTUNE)ds_train = ds_train.batch(batch_size=BATCH_SIZE, drop_remainder=True)ds_train = ds_train.prefetch(tf.data.AUTOTUNE)ds_test = ds_test.map(input_preprocess_test, num_parallel_calls=tf.data.AUTOTUNE)ds_test = ds_test.batch(batch_size=BATCH_SIZE, drop_remainder=True)

Training a model from scratch

We build an EfficientNetB0 with 120 output classes, that is initialized from scratch:

Note: the accuracy will increase very slowly and may overfit.

model = EfficientNetB0( include_top=True, weights=None, classes=NUM_CLASSES, input_shape=(IMG_SIZE, IMG_SIZE, 3),)model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])model.summary()epochs = 40 # @param {type: "slider", min:10, max:100}hist = model.fit(ds_train, epochs=epochs, validation_data=ds_test)

Model: "efficientnetb0"

┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩│ input_layer │ (None, 224, 224, │ 0 │ - ││ (InputLayer) │ 3) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ rescaling │ (None, 224, 224, │ 0 │ input_layer[0][0] ││ (Rescaling) │ 3) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ normalization │ (None, 224, 224, │ 7 │ rescaling[0][0] ││ (Normalization) │ 3) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ stem_conv_pad │ (None, 225, 225, │ 0 │ normalization[0][0] ││ (ZeroPadding2D) │ 3) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ stem_conv (Conv2D) │ (None, 112, 112, │ 864 │ stem_conv_pad[0][0] ││ │ 32) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ stem_bn │ (None, 112, 112, │ 128 │ stem_conv[0][0] ││ (BatchNormalizatio… │ 32) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ stem_activation │ (None, 112, 112, │ 0 │ stem_bn[0][0] ││ (Activation) │ 32) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block1a_dwconv │ (None, 112, 112, │ 288 │ stem_activation[0][… ││ (DepthwiseConv2D) │ 32) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block1a_bn │ (None, 112, 112, │ 128 │ block1a_dwconv[0][0] ││ (BatchNormalizatio… │ 32) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block1a_activation │ (None, 112, 112, │ 0 │ block1a_bn[0][0] ││ (Activation) │ 32) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block1a_se_squeeze │ (None, 32) │ 0 │ block1a_activation[… ││ (GlobalAveragePool… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block1a_se_reshape │ (None, 1, 1, 32) │ 0 │ block1a_se_squeeze[… ││ (Reshape) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block1a_se_reduce │ (None, 1, 1, 8) │ 264 │ block1a_se_reshape[… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block1a_se_expand │ (None, 1, 1, 32) │ 288 │ block1a_se_reduce[0… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block1a_se_excite │ (None, 112, 112, │ 0 │ block1a_activation[… ││ (Multiply) │ 32) │ │ block1a_se_expand[0… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block1a_project_co… │ (None, 112, 112, │ 512 │ block1a_se_excite[0… ││ (Conv2D) │ 16) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block1a_project_bn │ (None, 112, 112, │ 64 │ block1a_project_con… ││ (BatchNormalizatio… │ 16) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2a_expand_conv │ (None, 112, 112, │ 1,536 │ block1a_project_bn[… ││ (Conv2D) │ 96) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2a_expand_bn │ (None, 112, 112, │ 384 │ block2a_expand_conv… ││ (BatchNormalizatio… │ 96) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2a_expand_act… │ (None, 112, 112, │ 0 │ block2a_expand_bn[0… ││ (Activation) │ 96) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2a_dwconv_pad │ (None, 113, 113, │ 0 │ block2a_expand_acti… ││ (ZeroPadding2D) │ 96) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2a_dwconv │ (None, 56, 56, │ 864 │ block2a_dwconv_pad[… ││ (DepthwiseConv2D) │ 96) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2a_bn │ (None, 56, 56, │ 384 │ block2a_dwconv[0][0] ││ (BatchNormalizatio… │ 96) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2a_activation │ (None, 56, 56, │ 0 │ block2a_bn[0][0] ││ (Activation) │ 96) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2a_se_squeeze │ (None, 96) │ 0 │ block2a_activation[… ││ (GlobalAveragePool… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2a_se_reshape │ (None, 1, 1, 96) │ 0 │ block2a_se_squeeze[… ││ (Reshape) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2a_se_reduce │ (None, 1, 1, 4) │ 388 │ block2a_se_reshape[… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2a_se_expand │ (None, 1, 1, 96) │ 480 │ block2a_se_reduce[0… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2a_se_excite │ (None, 56, 56, │ 0 │ block2a_activation[… ││ (Multiply) │ 96) │ │ block2a_se_expand[0… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2a_project_co… │ (None, 56, 56, │ 2,304 │ block2a_se_excite[0… ││ (Conv2D) │ 24) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2a_project_bn │ (None, 56, 56, │ 96 │ block2a_project_con… ││ (BatchNormalizatio… │ 24) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2b_expand_conv │ (None, 56, 56, │ 3,456 │ block2a_project_bn[… ││ (Conv2D) │ 144) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2b_expand_bn │ (None, 56, 56, │ 576 │ block2b_expand_conv… ││ (BatchNormalizatio… │ 144) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2b_expand_act… │ (None, 56, 56, │ 0 │ block2b_expand_bn[0… ││ (Activation) │ 144) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2b_dwconv │ (None, 56, 56, │ 1,296 │ block2b_expand_acti… ││ (DepthwiseConv2D) │ 144) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2b_bn │ (None, 56, 56, │ 576 │ block2b_dwconv[0][0] ││ (BatchNormalizatio… │ 144) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2b_activation │ (None, 56, 56, │ 0 │ block2b_bn[0][0] ││ (Activation) │ 144) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2b_se_squeeze │ (None, 144) │ 0 │ block2b_activation[… ││ (GlobalAveragePool… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2b_se_reshape │ (None, 1, 1, 144) │ 0 │ block2b_se_squeeze[… ││ (Reshape) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2b_se_reduce │ (None, 1, 1, 6) │ 870 │ block2b_se_reshape[… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2b_se_expand │ (None, 1, 1, 144) │ 1,008 │ block2b_se_reduce[0… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2b_se_excite │ (None, 56, 56, │ 0 │ block2b_activation[… ││ (Multiply) │ 144) │ │ block2b_se_expand[0… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2b_project_co… │ (None, 56, 56, │ 3,456 │ block2b_se_excite[0… ││ (Conv2D) │ 24) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2b_project_bn │ (None, 56, 56, │ 96 │ block2b_project_con… ││ (BatchNormalizatio… │ 24) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2b_drop │ (None, 56, 56, │ 0 │ block2b_project_bn[… ││ (Dropout) │ 24) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block2b_add (Add) │ (None, 56, 56, │ 0 │ block2b_drop[0][0], ││ │ 24) │ │ block2a_project_bn[… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3a_expand_conv │ (None, 56, 56, │ 3,456 │ block2b_add[0][0] ││ (Conv2D) │ 144) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3a_expand_bn │ (None, 56, 56, │ 576 │ block3a_expand_conv… ││ (BatchNormalizatio… │ 144) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3a_expand_act… │ (None, 56, 56, │ 0 │ block3a_expand_bn[0… ││ (Activation) │ 144) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3a_dwconv_pad │ (None, 59, 59, │ 0 │ block3a_expand_acti… ││ (ZeroPadding2D) │ 144) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3a_dwconv │ (None, 28, 28, │ 3,600 │ block3a_dwconv_pad[… ││ (DepthwiseConv2D) │ 144) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3a_bn │ (None, 28, 28, │ 576 │ block3a_dwconv[0][0] ││ (BatchNormalizatio… │ 144) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3a_activation │ (None, 28, 28, │ 0 │ block3a_bn[0][0] ││ (Activation) │ 144) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3a_se_squeeze │ (None, 144) │ 0 │ block3a_activation[… ││ (GlobalAveragePool… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3a_se_reshape │ (None, 1, 1, 144) │ 0 │ block3a_se_squeeze[… ││ (Reshape) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3a_se_reduce │ (None, 1, 1, 6) │ 870 │ block3a_se_reshape[… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3a_se_expand │ (None, 1, 1, 144) │ 1,008 │ block3a_se_reduce[0… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3a_se_excite │ (None, 28, 28, │ 0 │ block3a_activation[… ││ (Multiply) │ 144) │ │ block3a_se_expand[0… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3a_project_co… │ (None, 28, 28, │ 5,760 │ block3a_se_excite[0… ││ (Conv2D) │ 40) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3a_project_bn │ (None, 28, 28, │ 160 │ block3a_project_con… ││ (BatchNormalizatio… │ 40) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3b_expand_conv │ (None, 28, 28, │ 9,600 │ block3a_project_bn[… ││ (Conv2D) │ 240) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3b_expand_bn │ (None, 28, 28, │ 960 │ block3b_expand_conv… ││ (BatchNormalizatio… │ 240) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3b_expand_act… │ (None, 28, 28, │ 0 │ block3b_expand_bn[0… ││ (Activation) │ 240) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3b_dwconv │ (None, 28, 28, │ 6,000 │ block3b_expand_acti… ││ (DepthwiseConv2D) │ 240) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3b_bn │ (None, 28, 28, │ 960 │ block3b_dwconv[0][0] ││ (BatchNormalizatio… │ 240) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3b_activation │ (None, 28, 28, │ 0 │ block3b_bn[0][0] ││ (Activation) │ 240) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3b_se_squeeze │ (None, 240) │ 0 │ block3b_activation[… ││ (GlobalAveragePool… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3b_se_reshape │ (None, 1, 1, 240) │ 0 │ block3b_se_squeeze[… ││ (Reshape) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3b_se_reduce │ (None, 1, 1, 10) │ 2,410 │ block3b_se_reshape[… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3b_se_expand │ (None, 1, 1, 240) │ 2,640 │ block3b_se_reduce[0… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3b_se_excite │ (None, 28, 28, │ 0 │ block3b_activation[… ││ (Multiply) │ 240) │ │ block3b_se_expand[0… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3b_project_co… │ (None, 28, 28, │ 9,600 │ block3b_se_excite[0… ││ (Conv2D) │ 40) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3b_project_bn │ (None, 28, 28, │ 160 │ block3b_project_con… ││ (BatchNormalizatio… │ 40) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3b_drop │ (None, 28, 28, │ 0 │ block3b_project_bn[… ││ (Dropout) │ 40) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block3b_add (Add) │ (None, 28, 28, │ 0 │ block3b_drop[0][0], ││ │ 40) │ │ block3a_project_bn[… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4a_expand_conv │ (None, 28, 28, │ 9,600 │ block3b_add[0][0] ││ (Conv2D) │ 240) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4a_expand_bn │ (None, 28, 28, │ 960 │ block4a_expand_conv… ││ (BatchNormalizatio… │ 240) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4a_expand_act… │ (None, 28, 28, │ 0 │ block4a_expand_bn[0… ││ (Activation) │ 240) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4a_dwconv_pad │ (None, 29, 29, │ 0 │ block4a_expand_acti… ││ (ZeroPadding2D) │ 240) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4a_dwconv │ (None, 14, 14, │ 2,160 │ block4a_dwconv_pad[… ││ (DepthwiseConv2D) │ 240) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4a_bn │ (None, 14, 14, │ 960 │ block4a_dwconv[0][0] ││ (BatchNormalizatio… │ 240) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4a_activation │ (None, 14, 14, │ 0 │ block4a_bn[0][0] ││ (Activation) │ 240) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4a_se_squeeze │ (None, 240) │ 0 │ block4a_activation[… ││ (GlobalAveragePool… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4a_se_reshape │ (None, 1, 1, 240) │ 0 │ block4a_se_squeeze[… ││ (Reshape) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4a_se_reduce │ (None, 1, 1, 10) │ 2,410 │ block4a_se_reshape[… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4a_se_expand │ (None, 1, 1, 240) │ 2,640 │ block4a_se_reduce[0… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4a_se_excite │ (None, 14, 14, │ 0 │ block4a_activation[… ││ (Multiply) │ 240) │ │ block4a_se_expand[0… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4a_project_co… │ (None, 14, 14, │ 19,200 │ block4a_se_excite[0… ││ (Conv2D) │ 80) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4a_project_bn │ (None, 14, 14, │ 320 │ block4a_project_con… ││ (BatchNormalizatio… │ 80) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4b_expand_conv │ (None, 14, 14, │ 38,400 │ block4a_project_bn[… ││ (Conv2D) │ 480) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4b_expand_bn │ (None, 14, 14, │ 1,920 │ block4b_expand_conv… ││ (BatchNormalizatio… │ 480) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4b_expand_act… │ (None, 14, 14, │ 0 │ block4b_expand_bn[0… ││ (Activation) │ 480) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4b_dwconv │ (None, 14, 14, │ 4,320 │ block4b_expand_acti… ││ (DepthwiseConv2D) │ 480) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4b_bn │ (None, 14, 14, │ 1,920 │ block4b_dwconv[0][0] ││ (BatchNormalizatio… │ 480) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4b_activation │ (None, 14, 14, │ 0 │ block4b_bn[0][0] ││ (Activation) │ 480) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4b_se_squeeze │ (None, 480) │ 0 │ block4b_activation[… ││ (GlobalAveragePool… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4b_se_reshape │ (None, 1, 1, 480) │ 0 │ block4b_se_squeeze[… ││ (Reshape) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4b_se_reduce │ (None, 1, 1, 20) │ 9,620 │ block4b_se_reshape[… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4b_se_expand │ (None, 1, 1, 480) │ 10,080 │ block4b_se_reduce[0… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4b_se_excite │ (None, 14, 14, │ 0 │ block4b_activation[… ││ (Multiply) │ 480) │ │ block4b_se_expand[0… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4b_project_co… │ (None, 14, 14, │ 38,400 │ block4b_se_excite[0… ││ (Conv2D) │ 80) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4b_project_bn │ (None, 14, 14, │ 320 │ block4b_project_con… ││ (BatchNormalizatio… │ 80) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4b_drop │ (None, 14, 14, │ 0 │ block4b_project_bn[… ││ (Dropout) │ 80) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4b_add (Add) │ (None, 14, 14, │ 0 │ block4b_drop[0][0], ││ │ 80) │ │ block4a_project_bn[… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4c_expand_conv │ (None, 14, 14, │ 38,400 │ block4b_add[0][0] ││ (Conv2D) │ 480) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4c_expand_bn │ (None, 14, 14, │ 1,920 │ block4c_expand_conv… ││ (BatchNormalizatio… │ 480) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4c_expand_act… │ (None, 14, 14, │ 0 │ block4c_expand_bn[0… ││ (Activation) │ 480) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4c_dwconv │ (None, 14, 14, │ 4,320 │ block4c_expand_acti… ││ (DepthwiseConv2D) │ 480) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4c_bn │ (None, 14, 14, │ 1,920 │ block4c_dwconv[0][0] ││ (BatchNormalizatio… │ 480) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4c_activation │ (None, 14, 14, │ 0 │ block4c_bn[0][0] ││ (Activation) │ 480) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4c_se_squeeze │ (None, 480) │ 0 │ block4c_activation[… ││ (GlobalAveragePool… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4c_se_reshape │ (None, 1, 1, 480) │ 0 │ block4c_se_squeeze[… ││ (Reshape) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4c_se_reduce │ (None, 1, 1, 20) │ 9,620 │ block4c_se_reshape[… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4c_se_expand │ (None, 1, 1, 480) │ 10,080 │ block4c_se_reduce[0… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4c_se_excite │ (None, 14, 14, │ 0 │ block4c_activation[… ││ (Multiply) │ 480) │ │ block4c_se_expand[0… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4c_project_co… │ (None, 14, 14, │ 38,400 │ block4c_se_excite[0… ││ (Conv2D) │ 80) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4c_project_bn │ (None, 14, 14, │ 320 │ block4c_project_con… ││ (BatchNormalizatio… │ 80) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4c_drop │ (None, 14, 14, │ 0 │ block4c_project_bn[… ││ (Dropout) │ 80) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block4c_add (Add) │ (None, 14, 14, │ 0 │ block4c_drop[0][0], ││ │ 80) │ │ block4b_add[0][0] │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5a_expand_conv │ (None, 14, 14, │ 38,400 │ block4c_add[0][0] ││ (Conv2D) │ 480) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5a_expand_bn │ (None, 14, 14, │ 1,920 │ block5a_expand_conv… ││ (BatchNormalizatio… │ 480) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5a_expand_act… │ (None, 14, 14, │ 0 │ block5a_expand_bn[0… ││ (Activation) │ 480) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5a_dwconv │ (None, 14, 14, │ 12,000 │ block5a_expand_acti… ││ (DepthwiseConv2D) │ 480) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5a_bn │ (None, 14, 14, │ 1,920 │ block5a_dwconv[0][0] ││ (BatchNormalizatio… │ 480) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5a_activation │ (None, 14, 14, │ 0 │ block5a_bn[0][0] ││ (Activation) │ 480) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5a_se_squeeze │ (None, 480) │ 0 │ block5a_activation[… ││ (GlobalAveragePool… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5a_se_reshape │ (None, 1, 1, 480) │ 0 │ block5a_se_squeeze[… ││ (Reshape) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5a_se_reduce │ (None, 1, 1, 20) │ 9,620 │ block5a_se_reshape[… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5a_se_expand │ (None, 1, 1, 480) │ 10,080 │ block5a_se_reduce[0… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5a_se_excite │ (None, 14, 14, │ 0 │ block5a_activation[… ││ (Multiply) │ 480) │ │ block5a_se_expand[0… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5a_project_co… │ (None, 14, 14, │ 53,760 │ block5a_se_excite[0… ││ (Conv2D) │ 112) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5a_project_bn │ (None, 14, 14, │ 448 │ block5a_project_con… ││ (BatchNormalizatio… │ 112) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5b_expand_conv │ (None, 14, 14, │ 75,264 │ block5a_project_bn[… ││ (Conv2D) │ 672) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5b_expand_bn │ (None, 14, 14, │ 2,688 │ block5b_expand_conv… ││ (BatchNormalizatio… │ 672) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5b_expand_act… │ (None, 14, 14, │ 0 │ block5b_expand_bn[0… ││ (Activation) │ 672) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5b_dwconv │ (None, 14, 14, │ 16,800 │ block5b_expand_acti… ││ (DepthwiseConv2D) │ 672) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5b_bn │ (None, 14, 14, │ 2,688 │ block5b_dwconv[0][0] ││ (BatchNormalizatio… │ 672) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5b_activation │ (None, 14, 14, │ 0 │ block5b_bn[0][0] ││ (Activation) │ 672) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5b_se_squeeze │ (None, 672) │ 0 │ block5b_activation[… ││ (GlobalAveragePool… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5b_se_reshape │ (None, 1, 1, 672) │ 0 │ block5b_se_squeeze[… ││ (Reshape) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5b_se_reduce │ (None, 1, 1, 28) │ 18,844 │ block5b_se_reshape[… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5b_se_expand │ (None, 1, 1, 672) │ 19,488 │ block5b_se_reduce[0… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5b_se_excite │ (None, 14, 14, │ 0 │ block5b_activation[… ││ (Multiply) │ 672) │ │ block5b_se_expand[0… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5b_project_co… │ (None, 14, 14, │ 75,264 │ block5b_se_excite[0… ││ (Conv2D) │ 112) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5b_project_bn │ (None, 14, 14, │ 448 │ block5b_project_con… ││ (BatchNormalizatio… │ 112) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5b_drop │ (None, 14, 14, │ 0 │ block5b_project_bn[… ││ (Dropout) │ 112) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5b_add (Add) │ (None, 14, 14, │ 0 │ block5b_drop[0][0], ││ │ 112) │ │ block5a_project_bn[… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5c_expand_conv │ (None, 14, 14, │ 75,264 │ block5b_add[0][0] ││ (Conv2D) │ 672) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5c_expand_bn │ (None, 14, 14, │ 2,688 │ block5c_expand_conv… ││ (BatchNormalizatio… │ 672) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5c_expand_act… │ (None, 14, 14, │ 0 │ block5c_expand_bn[0… ││ (Activation) │ 672) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5c_dwconv │ (None, 14, 14, │ 16,800 │ block5c_expand_acti… ││ (DepthwiseConv2D) │ 672) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5c_bn │ (None, 14, 14, │ 2,688 │ block5c_dwconv[0][0] ││ (BatchNormalizatio… │ 672) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5c_activation │ (None, 14, 14, │ 0 │ block5c_bn[0][0] ││ (Activation) │ 672) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5c_se_squeeze │ (None, 672) │ 0 │ block5c_activation[… ││ (GlobalAveragePool… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5c_se_reshape │ (None, 1, 1, 672) │ 0 │ block5c_se_squeeze[… ││ (Reshape) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5c_se_reduce │ (None, 1, 1, 28) │ 18,844 │ block5c_se_reshape[… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5c_se_expand │ (None, 1, 1, 672) │ 19,488 │ block5c_se_reduce[0… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5c_se_excite │ (None, 14, 14, │ 0 │ block5c_activation[… ││ (Multiply) │ 672) │ │ block5c_se_expand[0… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5c_project_co… │ (None, 14, 14, │ 75,264 │ block5c_se_excite[0… ││ (Conv2D) │ 112) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5c_project_bn │ (None, 14, 14, │ 448 │ block5c_project_con… ││ (BatchNormalizatio… │ 112) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5c_drop │ (None, 14, 14, │ 0 │ block5c_project_bn[… ││ (Dropout) │ 112) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block5c_add (Add) │ (None, 14, 14, │ 0 │ block5c_drop[0][0], ││ │ 112) │ │ block5b_add[0][0] │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6a_expand_conv │ (None, 14, 14, │ 75,264 │ block5c_add[0][0] ││ (Conv2D) │ 672) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6a_expand_bn │ (None, 14, 14, │ 2,688 │ block6a_expand_conv… ││ (BatchNormalizatio… │ 672) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6a_expand_act… │ (None, 14, 14, │ 0 │ block6a_expand_bn[0… ││ (Activation) │ 672) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6a_dwconv_pad │ (None, 17, 17, │ 0 │ block6a_expand_acti… ││ (ZeroPadding2D) │ 672) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6a_dwconv │ (None, 7, 7, 672) │ 16,800 │ block6a_dwconv_pad[… ││ (DepthwiseConv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6a_bn │ (None, 7, 7, 672) │ 2,688 │ block6a_dwconv[0][0] ││ (BatchNormalizatio… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6a_activation │ (None, 7, 7, 672) │ 0 │ block6a_bn[0][0] ││ (Activation) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6a_se_squeeze │ (None, 672) │ 0 │ block6a_activation[… ││ (GlobalAveragePool… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6a_se_reshape │ (None, 1, 1, 672) │ 0 │ block6a_se_squeeze[… ││ (Reshape) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6a_se_reduce │ (None, 1, 1, 28) │ 18,844 │ block6a_se_reshape[… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6a_se_expand │ (None, 1, 1, 672) │ 19,488 │ block6a_se_reduce[0… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6a_se_excite │ (None, 7, 7, 672) │ 0 │ block6a_activation[… ││ (Multiply) │ │ │ block6a_se_expand[0… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6a_project_co… │ (None, 7, 7, 192) │ 129,024 │ block6a_se_excite[0… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6a_project_bn │ (None, 7, 7, 192) │ 768 │ block6a_project_con… ││ (BatchNormalizatio… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6b_expand_conv │ (None, 7, 7, │ 221,184 │ block6a_project_bn[… ││ (Conv2D) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6b_expand_bn │ (None, 7, 7, │ 4,608 │ block6b_expand_conv… ││ (BatchNormalizatio… │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6b_expand_act… │ (None, 7, 7, │ 0 │ block6b_expand_bn[0… ││ (Activation) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6b_dwconv │ (None, 7, 7, │ 28,800 │ block6b_expand_acti… ││ (DepthwiseConv2D) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6b_bn │ (None, 7, 7, │ 4,608 │ block6b_dwconv[0][0] ││ (BatchNormalizatio… │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6b_activation │ (None, 7, 7, │ 0 │ block6b_bn[0][0] ││ (Activation) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6b_se_squeeze │ (None, 1152) │ 0 │ block6b_activation[… ││ (GlobalAveragePool… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6b_se_reshape │ (None, 1, 1, │ 0 │ block6b_se_squeeze[… ││ (Reshape) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6b_se_reduce │ (None, 1, 1, 48) │ 55,344 │ block6b_se_reshape[… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6b_se_expand │ (None, 1, 1, │ 56,448 │ block6b_se_reduce[0… ││ (Conv2D) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6b_se_excite │ (None, 7, 7, │ 0 │ block6b_activation[… ││ (Multiply) │ 1152) │ │ block6b_se_expand[0… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6b_project_co… │ (None, 7, 7, 192) │ 221,184 │ block6b_se_excite[0… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6b_project_bn │ (None, 7, 7, 192) │ 768 │ block6b_project_con… ││ (BatchNormalizatio… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6b_drop │ (None, 7, 7, 192) │ 0 │ block6b_project_bn[… ││ (Dropout) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6b_add (Add) │ (None, 7, 7, 192) │ 0 │ block6b_drop[0][0], ││ │ │ │ block6a_project_bn[… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6c_expand_conv │ (None, 7, 7, │ 221,184 │ block6b_add[0][0] ││ (Conv2D) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6c_expand_bn │ (None, 7, 7, │ 4,608 │ block6c_expand_conv… ││ (BatchNormalizatio… │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6c_expand_act… │ (None, 7, 7, │ 0 │ block6c_expand_bn[0… ││ (Activation) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6c_dwconv │ (None, 7, 7, │ 28,800 │ block6c_expand_acti… ││ (DepthwiseConv2D) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6c_bn │ (None, 7, 7, │ 4,608 │ block6c_dwconv[0][0] ││ (BatchNormalizatio… │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6c_activation │ (None, 7, 7, │ 0 │ block6c_bn[0][0] ││ (Activation) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6c_se_squeeze │ (None, 1152) │ 0 │ block6c_activation[… ││ (GlobalAveragePool… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6c_se_reshape │ (None, 1, 1, │ 0 │ block6c_se_squeeze[… ││ (Reshape) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6c_se_reduce │ (None, 1, 1, 48) │ 55,344 │ block6c_se_reshape[… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6c_se_expand │ (None, 1, 1, │ 56,448 │ block6c_se_reduce[0… ││ (Conv2D) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6c_se_excite │ (None, 7, 7, │ 0 │ block6c_activation[… ││ (Multiply) │ 1152) │ │ block6c_se_expand[0… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6c_project_co… │ (None, 7, 7, 192) │ 221,184 │ block6c_se_excite[0… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6c_project_bn │ (None, 7, 7, 192) │ 768 │ block6c_project_con… ││ (BatchNormalizatio… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6c_drop │ (None, 7, 7, 192) │ 0 │ block6c_project_bn[… ││ (Dropout) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6c_add (Add) │ (None, 7, 7, 192) │ 0 │ block6c_drop[0][0], ││ │ │ │ block6b_add[0][0] │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6d_expand_conv │ (None, 7, 7, │ 221,184 │ block6c_add[0][0] ││ (Conv2D) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6d_expand_bn │ (None, 7, 7, │ 4,608 │ block6d_expand_conv… ││ (BatchNormalizatio… │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6d_expand_act… │ (None, 7, 7, │ 0 │ block6d_expand_bn[0… ││ (Activation) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6d_dwconv │ (None, 7, 7, │ 28,800 │ block6d_expand_acti… ││ (DepthwiseConv2D) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6d_bn │ (None, 7, 7, │ 4,608 │ block6d_dwconv[0][0] ││ (BatchNormalizatio… │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6d_activation │ (None, 7, 7, │ 0 │ block6d_bn[0][0] ││ (Activation) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6d_se_squeeze │ (None, 1152) │ 0 │ block6d_activation[… ││ (GlobalAveragePool… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6d_se_reshape │ (None, 1, 1, │ 0 │ block6d_se_squeeze[… ││ (Reshape) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6d_se_reduce │ (None, 1, 1, 48) │ 55,344 │ block6d_se_reshape[… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6d_se_expand │ (None, 1, 1, │ 56,448 │ block6d_se_reduce[0… ││ (Conv2D) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6d_se_excite │ (None, 7, 7, │ 0 │ block6d_activation[… ││ (Multiply) │ 1152) │ │ block6d_se_expand[0… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6d_project_co… │ (None, 7, 7, 192) │ 221,184 │ block6d_se_excite[0… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6d_project_bn │ (None, 7, 7, 192) │ 768 │ block6d_project_con… ││ (BatchNormalizatio… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6d_drop │ (None, 7, 7, 192) │ 0 │ block6d_project_bn[… ││ (Dropout) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block6d_add (Add) │ (None, 7, 7, 192) │ 0 │ block6d_drop[0][0], ││ │ │ │ block6c_add[0][0] │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block7a_expand_conv │ (None, 7, 7, │ 221,184 │ block6d_add[0][0] ││ (Conv2D) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block7a_expand_bn │ (None, 7, 7, │ 4,608 │ block7a_expand_conv… ││ (BatchNormalizatio… │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block7a_expand_act… │ (None, 7, 7, │ 0 │ block7a_expand_bn[0… ││ (Activation) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block7a_dwconv │ (None, 7, 7, │ 10,368 │ block7a_expand_acti… ││ (DepthwiseConv2D) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block7a_bn │ (None, 7, 7, │ 4,608 │ block7a_dwconv[0][0] ││ (BatchNormalizatio… │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block7a_activation │ (None, 7, 7, │ 0 │ block7a_bn[0][0] ││ (Activation) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block7a_se_squeeze │ (None, 1152) │ 0 │ block7a_activation[… ││ (GlobalAveragePool… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block7a_se_reshape │ (None, 1, 1, │ 0 │ block7a_se_squeeze[… ││ (Reshape) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block7a_se_reduce │ (None, 1, 1, 48) │ 55,344 │ block7a_se_reshape[… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block7a_se_expand │ (None, 1, 1, │ 56,448 │ block7a_se_reduce[0… ││ (Conv2D) │ 1152) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block7a_se_excite │ (None, 7, 7, │ 0 │ block7a_activation[… ││ (Multiply) │ 1152) │ │ block7a_se_expand[0… │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block7a_project_co… │ (None, 7, 7, 320) │ 368,640 │ block7a_se_excite[0… ││ (Conv2D) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ block7a_project_bn │ (None, 7, 7, 320) │ 1,280 │ block7a_project_con… ││ (BatchNormalizatio… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ top_conv (Conv2D) │ (None, 7, 7, │ 409,600 │ block7a_project_bn[… ││ │ 1280) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ top_bn │ (None, 7, 7, │ 5,120 │ top_conv[0][0] ││ (BatchNormalizatio… │ 1280) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ top_activation │ (None, 7, 7, │ 0 │ top_bn[0][0] ││ (Activation) │ 1280) │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ avg_pool │ (None, 1280) │ 0 │ top_activation[0][0] ││ (GlobalAveragePool… │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ top_dropout │ (None, 1280) │ 0 │ avg_pool[0][0] ││ (Dropout) │ │ │ │├─────────────────────┼───────────────────┼─────────┼──────────────────────┤│ predictions (Dense) │ (None, 120) │ 153,720 │ top_dropout[0][0] │└─────────────────────┴───────────────────┴─────────┴──────────────────────┘

 Total params: 4,203,291 (16.03 MB)

 Trainable params: 4,161,268 (15.87 MB)

 Non-trainable params: 42,023 (164.16 KB)

Epoch 1/40 1/187 [37m━━━━━━━━━━━━━━━━━━━━ 5:30:13 107s/step - accuracy: 0.0000e+00 - loss: 5.1065WARNING: All log messages before absl::InitializeLog() is called are written to STDERRI0000 00:00:1700241724.682725 1549299 device_compiler.h:187] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process. 187/187 ━━━━━━━━━━━━━━━━━━━━ 200s 501ms/step - accuracy: 0.0097 - loss: 5.0567 - val_accuracy: 0.0100 - val_loss: 4.9278Epoch 2/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 95s 507ms/step - accuracy: 0.0214 - loss: 4.6918 - val_accuracy: 0.0141 - val_loss: 5.5380Epoch 3/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 89s 474ms/step - accuracy: 0.0298 - loss: 4.4749 - val_accuracy: 0.0375 - val_loss: 4.4576Epoch 4/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 90s 479ms/step - accuracy: 0.0423 - loss: 4.3206 - val_accuracy: 0.0391 - val_loss: 4.9898Epoch 5/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 89s 473ms/step - accuracy: 0.0458 - loss: 4.2312 - val_accuracy: 0.0416 - val_loss: 4.3210Epoch 6/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 141s 470ms/step - accuracy: 0.0579 - loss: 4.1162 - val_accuracy: 0.0540 - val_loss: 4.3371Epoch 7/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 89s 476ms/step - accuracy: 0.0679 - loss: 4.0150 - val_accuracy: 0.0786 - val_loss: 3.9759Epoch 8/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 89s 477ms/step - accuracy: 0.0828 - loss: 3.9147 - val_accuracy: 0.0651 - val_loss: 4.1641Epoch 9/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 142s 475ms/step - accuracy: 0.0932 - loss: 3.8297 - val_accuracy: 0.0928 - val_loss: 3.8985Epoch 10/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 88s 472ms/step - accuracy: 0.1092 - loss: 3.7321 - val_accuracy: 0.0946 - val_loss: 3.8618Epoch 11/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 89s 476ms/step - accuracy: 0.1245 - loss: 3.6451 - val_accuracy: 0.0880 - val_loss: 3.9584Epoch 12/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 92s 493ms/step - accuracy: 0.1457 - loss: 3.5514 - val_accuracy: 0.1096 - val_loss: 3.8184Epoch 13/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 88s 471ms/step - accuracy: 0.1606 - loss: 3.4654 - val_accuracy: 0.1118 - val_loss: 3.8059Epoch 14/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 87s 464ms/step - accuracy: 0.1660 - loss: 3.3826 - val_accuracy: 0.1472 - val_loss: 3.5726Epoch 15/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 146s 485ms/step - accuracy: 0.1815 - loss: 3.2935 - val_accuracy: 0.1154 - val_loss: 3.8134Epoch 16/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 87s 466ms/step - accuracy: 0.1942 - loss: 3.2218 - val_accuracy: 0.1540 - val_loss: 3.5051Epoch 17/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 88s 471ms/step - accuracy: 0.2131 - loss: 3.1427 - val_accuracy: 0.1381 - val_loss: 3.7206Epoch 18/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 87s 467ms/step - accuracy: 0.2264 - loss: 3.0461 - val_accuracy: 0.1707 - val_loss: 3.4122Epoch 19/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 88s 470ms/step - accuracy: 0.2401 - loss: 2.9821 - val_accuracy: 0.1515 - val_loss: 3.6481Epoch 20/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 88s 469ms/step - accuracy: 0.2613 - loss: 2.8815 - val_accuracy: 0.1783 - val_loss: 3.4767Epoch 21/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 91s 485ms/step - accuracy: 0.2741 - loss: 2.8102 - val_accuracy: 0.1927 - val_loss: 3.3183Epoch 22/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 90s 477ms/step - accuracy: 0.2892 - loss: 2.7408 - val_accuracy: 0.1859 - val_loss: 3.4887Epoch 23/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 91s 485ms/step - accuracy: 0.3093 - loss: 2.6526 - val_accuracy: 0.1924 - val_loss: 3.4622Epoch 24/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 92s 491ms/step - accuracy: 0.3201 - loss: 2.5750 - val_accuracy: 0.2253 - val_loss: 3.1873Epoch 25/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 95s 508ms/step - accuracy: 0.3280 - loss: 2.5150 - val_accuracy: 0.2148 - val_loss: 3.3391Epoch 26/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 92s 490ms/step - accuracy: 0.3465 - loss: 2.4402 - val_accuracy: 0.2270 - val_loss: 3.2679Epoch 27/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 93s 494ms/step - accuracy: 0.3735 - loss: 2.3199 - val_accuracy: 0.2080 - val_loss: 3.5687Epoch 28/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 89s 476ms/step - accuracy: 0.3837 - loss: 2.2645 - val_accuracy: 0.2374 - val_loss: 3.3592Epoch 29/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 142s 474ms/step - accuracy: 0.3962 - loss: 2.2110 - val_accuracy: 0.2008 - val_loss: 3.6071Epoch 30/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 87s 466ms/step - accuracy: 0.4175 - loss: 2.1086 - val_accuracy: 0.2302 - val_loss: 3.4161Epoch 31/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 87s 465ms/step - accuracy: 0.4359 - loss: 2.0610 - val_accuracy: 0.2231 - val_loss: 3.5957Epoch 32/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 148s 498ms/step - accuracy: 0.4463 - loss: 1.9866 - val_accuracy: 0.2234 - val_loss: 3.7263Epoch 33/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 92s 489ms/step - accuracy: 0.4613 - loss: 1.8821 - val_accuracy: 0.2239 - val_loss: 3.6929Epoch 34/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 139s 475ms/step - accuracy: 0.4925 - loss: 1.7858 - val_accuracy: 0.2238 - val_loss: 3.8351Epoch 35/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 91s 485ms/step - accuracy: 0.5105 - loss: 1.7074 - val_accuracy: 0.1930 - val_loss: 4.1941Epoch 36/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 140s 474ms/step - accuracy: 0.5334 - loss: 1.6256 - val_accuracy: 0.2098 - val_loss: 4.1464Epoch 37/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 87s 464ms/step - accuracy: 0.5504 - loss: 1.5603 - val_accuracy: 0.2306 - val_loss: 4.0215Epoch 38/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 90s 480ms/step - accuracy: 0.5736 - loss: 1.4419 - val_accuracy: 0.2240 - val_loss: 4.1604Epoch 39/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 91s 486ms/step - accuracy: 0.6025 - loss: 1.3612 - val_accuracy: 0.2344 - val_loss: 4.0505Epoch 40/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 89s 474ms/step - accuracy: 0.6199 - loss: 1.2889 - val_accuracy: 0.2151 - val_loss: 4.3660

Training the model is relatively fast. This might make it sounds easy to simply train EfficientNet on anydataset wanted from scratch. However, training EfficientNet on smaller datasets,especially those with lower resolution like CIFAR-100, faces the significant challenge ofoverfitting.

Hence training from scratch requires very careful choice of hyperparameters and isdifficult to find suitable regularization. It would also be much more demanding in resources.Plotting the training and validation accuracymakes it clear that validation accuracy stagnates at a low value.

import matplotlib.pyplot as pltdef plot_hist(hist): plt.plot(hist.history["accuracy"]) plt.plot(hist.history["val_accuracy"]) plt.title("model accuracy") plt.ylabel("accuracy") plt.xlabel("epoch") plt.legend(["train", "validation"], loc="upper left") plt.show()plot_hist(hist)

Transfer learning from pre-trained weights

Here we initialize the model with pre-trained ImageNet weights,and we fine-tune it on our own dataset.

def build_model(num_classes): inputs = layers.Input(shape=(IMG_SIZE, IMG_SIZE, 3)) model = EfficientNetB0(include_top=False, input_tensor=inputs, weights="imagenet") # Freeze the pretrained weights model.trainable = False # Rebuild top x = layers.GlobalAveragePooling2D(name="avg_pool")(model.output) x = layers.BatchNormalization()(x) top_dropout_rate = 0.2 x = layers.Dropout(top_dropout_rate, name="top_dropout")(x) outputs = layers.Dense(num_classes, activation="softmax", name="pred")(x) # Compile model = keras.Model(inputs, outputs, name="EfficientNet") optimizer = keras.optimizers.Adam(learning_rate=1e-2) model.compile( optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"] ) return model

The first step to transfer learning is to freeze all layers and train only the toplayers. For this step, a relatively large learning rate (1e-2) can be used.Note that validation accuracy and loss will usually be better than trainingaccuracy and loss. This is because the regularization is strong, which onlysuppresses training-time metrics.

Note that the convergence may take up to 50 epochs depending on choice of learning rate.If image augmentation layers were notapplied, the validation accuracy may only reach ~60%.

model = build_model(num_classes=NUM_CLASSES)epochs = 25 # @param {type: "slider", min:8, max:80}hist = model.fit(ds_train, epochs=epochs, validation_data=ds_test)plot_hist(hist)

Epoch 1/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 108s 432ms/step - accuracy: 0.2654 - loss: 4.3710 - val_accuracy: 0.6888 - val_loss: 1.0875Epoch 2/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 119s 412ms/step - accuracy: 0.4863 - loss: 2.0996 - val_accuracy: 0.7282 - val_loss: 0.9072Epoch 3/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 78s 416ms/step - accuracy: 0.5422 - loss: 1.7120 - val_accuracy: 0.7411 - val_loss: 0.8574Epoch 4/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 77s 412ms/step - accuracy: 0.5509 - loss: 1.6472 - val_accuracy: 0.7451 - val_loss: 0.8457Epoch 5/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 81s 431ms/step - accuracy: 0.5744 - loss: 1.5373 - val_accuracy: 0.7424 - val_loss: 0.8649Epoch 6/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 78s 417ms/step - accuracy: 0.5715 - loss: 1.5595 - val_accuracy: 0.7374 - val_loss: 0.8736Epoch 7/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 81s 432ms/step - accuracy: 0.5802 - loss: 1.5045 - val_accuracy: 0.7430 - val_loss: 0.8675Epoch 8/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 77s 411ms/step - accuracy: 0.5839 - loss: 1.4972 - val_accuracy: 0.7392 - val_loss: 0.8647Epoch 9/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 77s 411ms/step - accuracy: 0.5929 - loss: 1.4699 - val_accuracy: 0.7508 - val_loss: 0.8634Epoch 10/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 82s 437ms/step - accuracy: 0.6040 - loss: 1.4442 - val_accuracy: 0.7520 - val_loss: 0.8480Epoch 11/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 78s 416ms/step - accuracy: 0.5972 - loss: 1.4626 - val_accuracy: 0.7379 - val_loss: 0.8879Epoch 12/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 79s 421ms/step - accuracy: 0.5965 - loss: 1.4700 - val_accuracy: 0.7383 - val_loss: 0.9409Epoch 13/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 82s 420ms/step - accuracy: 0.6034 - loss: 1.4533 - val_accuracy: 0.7474 - val_loss: 0.8922Epoch 14/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 81s 435ms/step - accuracy: 0.6053 - loss: 1.4170 - val_accuracy: 0.7416 - val_loss: 0.9119Epoch 15/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 77s 411ms/step - accuracy: 0.6059 - loss: 1.4125 - val_accuracy: 0.7406 - val_loss: 0.9205Epoch 16/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 82s 438ms/step - accuracy: 0.5979 - loss: 1.4554 - val_accuracy: 0.7392 - val_loss: 0.9120Epoch 17/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 77s 411ms/step - accuracy: 0.6081 - loss: 1.4089 - val_accuracy: 0.7423 - val_loss: 0.9305Epoch 18/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 82s 436ms/step - accuracy: 0.6041 - loss: 1.4390 - val_accuracy: 0.7380 - val_loss: 0.9644Epoch 19/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 79s 417ms/step - accuracy: 0.6018 - loss: 1.4324 - val_accuracy: 0.7439 - val_loss: 0.9129Epoch 20/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 81s 430ms/step - accuracy: 0.6057 - loss: 1.4342 - val_accuracy: 0.7305 - val_loss: 0.9463Epoch 21/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 77s 410ms/step - accuracy: 0.6209 - loss: 1.3824 - val_accuracy: 0.7410 - val_loss: 0.9503Epoch 22/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 78s 419ms/step - accuracy: 0.6170 - loss: 1.4246 - val_accuracy: 0.7336 - val_loss: 0.9606Epoch 23/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 85s 455ms/step - accuracy: 0.6153 - loss: 1.4009 - val_accuracy: 0.7334 - val_loss: 0.9520Epoch 24/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 82s 438ms/step - accuracy: 0.6051 - loss: 1.4343 - val_accuracy: 0.7435 - val_loss: 0.9403Epoch 25/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 138s 416ms/step - accuracy: 0.6065 - loss: 1.4131 - val_accuracy: 0.7456 - val_loss: 0.9307

The second step is to unfreeze a number of layers and fit the model using smallerlearning rate. In this example we show unfreezing all layers, but depending onspecific dataset it may be desireble to only unfreeze a fraction of all layers.

When the feature extraction withpretrained model works good enough, this step would give a very limited gain onvalidation accuracy. In our case we only see a small improvement,as ImageNet pretraining already exposed the model to a good amount of dogs.

On the other hand, when we use pretrained weights on a dataset that is more differentfrom ImageNet, this fine-tuning step can be crucial as the feature extractor alsoneeds to be adjusted by a considerable amount. Such a situation can be demonstratedif choosing CIFAR-100 dataset instead, where fine-tuning boosts validation accuracyby about 10% to pass 80% on EfficientNetB0.

A side note on freezing/unfreezing models: setting trainable of a Model willsimultaneously set all layers belonging to the Model to the same trainableattribute. Each layer is trainable only if both the layer itself and the modelcontaining it are trainable. Hence when we need to partially freeze/unfreezea model, we need to make sure the trainable attribute of the model is setto True.

def unfreeze_model(model): # We unfreeze the top 20 layers while leaving BatchNorm layers frozen for layer in model.layers[-20:]: if not isinstance(layer, layers.BatchNormalization): layer.trainable = True optimizer = keras.optimizers.Adam(learning_rate=1e-5) model.compile( optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"] )unfreeze_model(model)epochs = 4 # @param {type: "slider", min:4, max:10}hist = model.fit(ds_train, epochs=epochs, validation_data=ds_test)plot_hist(hist)

Epoch 1/4 187/187 ━━━━━━━━━━━━━━━━━━━━ 111s 442ms/step - accuracy: 0.6310 - loss: 1.3425 - val_accuracy: 0.7565 - val_loss: 0.8874Epoch 2/4 187/187 ━━━━━━━━━━━━━━━━━━━━ 77s 413ms/step - accuracy: 0.6518 - loss: 1.2755 - val_accuracy: 0.7635 - val_loss: 0.8588Epoch 3/4 187/187 ━━━━━━━━━━━━━━━━━━━━ 82s 437ms/step - accuracy: 0.6491 - loss: 1.2426 - val_accuracy: 0.7663 - val_loss: 0.8419Epoch 4/4 187/187 ━━━━━━━━━━━━━━━━━━━━ 79s 419ms/step - accuracy: 0.6625 - loss: 1.1775 - val_accuracy: 0.7701 - val_loss: 0.8284

Tips for fine tuning EfficientNet

On unfreezing layers:

• The BatchNormalization layers need to be kept frozen(more details).If they are also turned to trainable, thefirst epoch after unfreezing will significantly reduce accuracy.
• In some cases it may be beneficial to open up only a portion of layers instead ofunfreezing all. This will make fine tuning much faster when going to larger models likeB7.
• Each block needs to be all turned on or off. This is because the architecture includesa shortcut from the first layer to the last layer for each block. Not respecting blocksalso significantly harms the final performance.

Some other tips for utilizing EfficientNet:

• Larger variants of EfficientNet do not guarantee improved performance, especially fortasks with less data or fewer classes. In such a case, the larger variant of EfficientNetchosen, the harder it is to tune hyperparameters.
• EMA (Exponential Moving Average) is very helpful in training EfficientNet from scratch,but not so much for transfer learning.
• Do not use the RMSprop setup as in the original paper for transfer learning. Themomentum and learning rate are too high for transfer learning. It will easily corrupt thepretrained weight and blow up the loss. A quick check is to see if loss (as categoricalcross entropy) is getting significantly larger than log(NUM_CLASSES) after the sameepoch. If so, the initial learning rate/momentum is too high.
• Smaller batch size benefit validation accuracy, possibly due to effectively providingregularization.