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id="image-classification-via-finetuning-with-efficientnet">Image classification via fine-tuning with EfficientNet</h1> Author: <a href="https://github.com/yixingfu">Yixing Fu</a> Date created: 2020/06/30 Last modified: 2023/07/10 Description: Use EfficientNet with weights pre-trained on imagenet for Stanford Dogs classification. <div class='example_version_banner keras_3'>ⓘ This example uses Keras 3</div> <img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> <a href="https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/vision/ipynb/image_classification_efficientnet_fine_tuning.ipynb">View in Colab</a> •<img class="k-inline-icon" src="https://github.com/favicon.ico"/> <a href="https://github.com/keras-team/keras-io/blob/master/examples/vision/image_classification_efficientnet_fine_tuning.py">GitHub source</a> <hr /> <h2 id="introduction-what-is-efficientnet">Introduction: what is EfficientNet</h2> EfficientNet, first introduced in <a href="https://arxiv.org/abs/1905.11946">Tan and Le, 2019</a> is among the most efficient models (i.e. requiring least FLOPS for inference) that reaches State-of-the-Art accuracy on both imagenet and common image classification transfer learning tasks. The smallest base model is similar to <a href="https://arxiv.org/abs/1807.11626">MnasNet</a>, which reached near-SOTA with a significantly smaller model. By introducing a heuristic way to scale the model, EfficientNet provides a family of models (B0 to B7) that represents a good combination of efficiency and accuracy on a variety of scales. Such a scaling heuristics (compound-scaling, details see <a href="https://arxiv.org/abs/1905.11946">Tan and Le, 2019</a>) allows the efficiency-oriented base model (B0) to surpass models at every scale, while avoiding extensive grid-search of hyperparameters. A summary of the latest updates on the model is available at <a href="https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet">here</a>, where various augmentation schemes and semi-supervised learning approaches are applied to further improve the imagenet performance of the models. These extensions of the model can be used by updating weights without changing model architecture. <hr /> <h2 id="b0-to-b7-variants-of-efficientnet">B0 to B7 variants of EfficientNet</h2> (This section provides some details on "compound scaling", and can be skipped if you're only interested in using the models) Based on the <a href="https://arxiv.org/abs/1905.11946">original paper</a> people may have the impression that EfficientNet is a continuous family of models created by arbitrarily choosing scaling factor in as Eq.(3) of the paper. However, choice of resolution, depth and width are also restricted by many factors: <ul> <li>Resolution: Resolutions not divisible by 8, 16, etc. cause zero-padding near boundaries of some layers which wastes computational resources. This especially applies to smaller variants of the model, hence the input resolution for B0 and B1 are chosen as 224 and 240.</li> </ul> <ul> <li>Depth and width: The building blocks of EfficientNet demands channel size to be multiples of 8.</li> </ul> <ul> <li>Resource limit: Memory limitation may bottleneck resolution when depth and width can still increase. In such a situation, increasing depth and/or width but keep resolution can still improve performance.</li> </ul> As a result, the depth, width and resolution of each variant of the EfficientNet models are hand-picked and proven to produce good results, though they may be significantly off 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. <hr /> <h2 id="keras-implementation-of-efficientnet">Keras implementation of EfficientNet</h2> An implementation of EfficientNet B0 to B7 has been shipped with Keras since v2.3. To use EfficientNetB0 for classifying 1000 classes of images from ImageNet, run: <div class="codehilite"><pre><code>from tensorflow.keras.applications import EfficientNetB0 model = EfficientNetB0(weights='imagenet') </code></pre></div> This model takes input images of shape <code>(224, 224, 3)</code>, and the input data should be in the range <code>[0, 255]</code>. Normalization is included as part of the model. Because training EfficientNet on ImageNet takes a tremendous amount of resources and several techniques that are not a part of the model architecture itself. Hence the Keras implementation by default loads pre-trained weights obtained via training with <a href="https://arxiv.org/abs/1805.09501">AutoAugment</a>. For B0 to B7 base models, the input shapes are different. Here is a list of input shape expected for each model: <table> <thead> <tr> <th>Base model</th> <th>resolution</th> </tr> </thead> <tbody> <tr> <td>EfficientNetB0</td> <td>224</td> </tr> <tr> <td>EfficientNetB1</td> <td>240</td> </tr> <tr> <td>EfficientNetB2</td> <td>260</td> </tr> <tr> <td>EfficientNetB3</td> <td>300</td> </tr> <tr> <td>EfficientNetB4</td> <td>380</td> </tr> <tr> <td>EfficientNetB5</td> <td>456</td> </tr> <tr> <td>EfficientNetB6</td> <td>528</td> </tr> <tr> <td>EfficientNetB7</td> <td>600</td> </tr> </tbody> </table> When the model is intended for transfer learning, the Keras implementation provides a option to remove the top layers: <div class="codehilite"><pre><code>model = EfficientNetB0(include_top=False, weights='imagenet') </code></pre></div> This option excludes the final <code>Dense</code> layer that turns 1280 features on the penultimate layer into prediction of the 1000 ImageNet classes. Replacing the top layer with custom layers allows using EfficientNet as a feature extractor in a transfer learning workflow. Another argument in the model constructor worth noticing is <code>drop_connect_rate</code> which controls the dropout rate responsible for <a href="https://arxiv.org/abs/1603.09382">stochastic depth</a>. This parameter serves as a toggle for extra regularization in finetuning, but does not affect loaded weights. For example, when stronger regularization is desired, try: <div class="codehilite"><pre><code>model = EfficientNetB0(weights='imagenet', drop_connect_rate=0.4) </code></pre></div> The default value is 0.2. <hr /> <h2 id="example-efficientnetb0-for-stanford-dogs">Example: EfficientNetB0 for Stanford Dogs.</h2> 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 on <a href="http://vision.stanford.edu/aditya86/ImageNetDogs/main.html">Stanford Dogs</a> dataset. <hr /> <h2 id="setup-and-data-loading">Setup and data loading</h2> <div class="codehilite"><pre><code>import numpy as np import tensorflow_datasets as tfds import tensorflow as tf # For tf.data import matplotlib.pyplot as plt import keras from keras import layers from keras.applications import EfficientNetB0 # IMG_SIZE is determined by EfficientNet model choice IMG_SIZE = 224 BATCH_SIZE = 64 </code></pre></div> <h3 id="loading-data">Loading data</h3> Here we load data from <a href="https://www.tensorflow.org/datasets">tensorflow_datasets</a> (hereafter TFDS). Stanford Dogs dataset is provided in TFDS as <a href="https://www.tensorflow.org/datasets/catalog/stanford_dogs">stanford_dogs</a>. 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 <code>dataset_name</code> below, you may also try this notebook for other datasets in TFDS such as <a href="https://www.tensorflow.org/datasets/catalog/cifar10">cifar10</a>, <a href="https://www.tensorflow.org/datasets/catalog/cifar100">cifar100</a>, <a href="https://www.tensorflow.org/datasets/catalog/food101">food101</a>, etc. When the images are much smaller than the size of EfficientNet input, we can simply upsample the input images. It has been shown in <a href="https://arxiv.org/abs/1905.11946">Tan and Le, 2019</a> that transfer learning result is better for increased resolution even if input images remain small. <div class="codehilite"><pre><code>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 </code></pre></div> When the dataset include images with various size, we need to resize them into a shared size. The Stanford Dogs dataset includes only images at least 200x200 pixels in size. Here we resize the images to the input size needed for EfficientNet. <div class="codehilite"><pre><code>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)) </code></pre></div> <h3 id="visualizing-the-data">Visualizing the data</h3> The following code shows the first 9 images with their labels. <div class="codehilite"><pre><code>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") </code></pre></div> <img alt="png" src="/img/examples/vision/image_classification_efficientnet_fine_tuning/image_classification_efficientnet_fine_tuning_9_0.png" /> <h3 id="data-augmentation">Data augmentation</h3> We can use the preprocessing layers APIs for image augmentation. <div class="codehilite"><pre><code>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 </code></pre></div> This <code>Sequential</code> model object can be used both as a part of the model we later build, and as a function to preprocess data before feeding into the model. Using them as function makes it easy to visualize the augmented images. Here we plot 9 examples of augmentation result of a given figure. <div class="codehilite"><pre><code>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") </code></pre></div> <img alt="png" src="/img/examples/vision/image_classification_efficientnet_fine_tuning/image_classification_efficientnet_fine_tuning_13_0.png" /> <h3 id="prepare-inputs">Prepare inputs</h3> Once we verify the input data and augmentation are working correctly, we prepare dataset for training. The input data are resized to uniform <code>IMG_SIZE</code>. The labels are put into one-hot (a.k.a. categorical) encoding. The dataset is batched. Note: <code>prefetch</code> and <code>AUTOTUNE</code> may in some situation improve performance, but depends on environment and the specific dataset used. See this <a href="https://www.tensorflow.org/guide/data_performance">guide</a> for more information on data pipeline performance. <div class="codehilite"><pre><code># One-hot / categorical encoding def input_preprocess_train(image, label): image = img_augmentation(image) label = tf.one_hot(label, NUM_CLASSES) return image, label def input_preprocess_test(image, label): label = tf.one_hot(label, NUM_CLASSES) return image, label ds_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) </code></pre></div> <hr /> <h2 id="training-a-model-from-scratch">Training a model from scratch</h2> We build an EfficientNetB0 with 120 output classes, that is initialized from scratch: Note: the accuracy will increase very slowly and may overfit. <div class="codehilite"><pre><code>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) </code></pre></div> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">Model: "efficientnetb0" </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ ┃ 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] │ └─────────────────────┴───────────────────┴─────────┴──────────────────────┘ </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Total params: 4,203,291 (16.03 MB) </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Trainable params: 4,161,268 (15.87 MB) </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Non-trainable params: 42,023 (164.16 KB) </pre> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 1/40 1/187 [37m━━━━━━━━━━━━━━━━━━━━ 5:30:13 107s/step - accuracy: 0.0000e+00 - loss: 5.1065 WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 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: 4.9278 Epoch 2/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 95s 507ms/step - accuracy: 0.0214 - loss: 4.6918 - val_accuracy: 0.0141 - val_loss: 5.5380 Epoch 3/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 89s 474ms/step - accuracy: 0.0298 - loss: 4.4749 - val_accuracy: 0.0375 - val_loss: 4.4576 Epoch 4/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 90s 479ms/step - accuracy: 0.0423 - loss: 4.3206 - val_accuracy: 0.0391 - val_loss: 4.9898 Epoch 5/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 89s 473ms/step - accuracy: 0.0458 - loss: 4.2312 - val_accuracy: 0.0416 - val_loss: 4.3210 Epoch 6/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 141s 470ms/step - accuracy: 0.0579 - loss: 4.1162 - val_accuracy: 0.0540 - val_loss: 4.3371 Epoch 7/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 89s 476ms/step - accuracy: 0.0679 - loss: 4.0150 - val_accuracy: 0.0786 - val_loss: 3.9759 Epoch 8/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 89s 477ms/step - accuracy: 0.0828 - loss: 3.9147 - val_accuracy: 0.0651 - val_loss: 4.1641 Epoch 9/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 142s 475ms/step - accuracy: 0.0932 - loss: 3.8297 - val_accuracy: 0.0928 - val_loss: 3.8985 Epoch 10/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 88s 472ms/step - accuracy: 0.1092 - loss: 3.7321 - val_accuracy: 0.0946 - val_loss: 3.8618 Epoch 11/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 89s 476ms/step - accuracy: 0.1245 - loss: 3.6451 - val_accuracy: 0.0880 - val_loss: 3.9584 Epoch 12/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 92s 493ms/step - accuracy: 0.1457 - loss: 3.5514 - val_accuracy: 0.1096 - val_loss: 3.8184 Epoch 13/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 88s 471ms/step - accuracy: 0.1606 - loss: 3.4654 - val_accuracy: 0.1118 - val_loss: 3.8059 Epoch 14/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 87s 464ms/step - accuracy: 0.1660 - loss: 3.3826 - val_accuracy: 0.1472 - val_loss: 3.5726 Epoch 15/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 146s 485ms/step - accuracy: 0.1815 - loss: 3.2935 - val_accuracy: 0.1154 - val_loss: 3.8134 Epoch 16/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 87s 466ms/step - accuracy: 0.1942 - loss: 3.2218 - val_accuracy: 0.1540 - val_loss: 3.5051 Epoch 17/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 88s 471ms/step - accuracy: 0.2131 - loss: 3.1427 - val_accuracy: 0.1381 - val_loss: 3.7206 Epoch 18/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 87s 467ms/step - accuracy: 0.2264 - loss: 3.0461 - val_accuracy: 0.1707 - val_loss: 3.4122 Epoch 19/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 88s 470ms/step - accuracy: 0.2401 - loss: 2.9821 - val_accuracy: 0.1515 - val_loss: 3.6481 Epoch 20/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 88s 469ms/step - accuracy: 0.2613 - loss: 2.8815 - val_accuracy: 0.1783 - val_loss: 3.4767 Epoch 21/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 91s 485ms/step - accuracy: 0.2741 - loss: 2.8102 - val_accuracy: 0.1927 - val_loss: 3.3183 Epoch 22/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 90s 477ms/step - accuracy: 0.2892 - loss: 2.7408 - val_accuracy: 0.1859 - val_loss: 3.4887 Epoch 23/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 91s 485ms/step - accuracy: 0.3093 - loss: 2.6526 - val_accuracy: 0.1924 - val_loss: 3.4622 Epoch 24/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 92s 491ms/step - accuracy: 0.3201 - loss: 2.5750 - val_accuracy: 0.2253 - val_loss: 3.1873 Epoch 25/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 95s 508ms/step - accuracy: 0.3280 - loss: 2.5150 - val_accuracy: 0.2148 - val_loss: 3.3391 Epoch 26/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 92s 490ms/step - accuracy: 0.3465 - loss: 2.4402 - val_accuracy: 0.2270 - val_loss: 3.2679 Epoch 27/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 93s 494ms/step - accuracy: 0.3735 - loss: 2.3199 - val_accuracy: 0.2080 - val_loss: 3.5687 Epoch 28/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 89s 476ms/step - accuracy: 0.3837 - loss: 2.2645 - val_accuracy: 0.2374 - val_loss: 3.3592 Epoch 29/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 142s 474ms/step - accuracy: 0.3962 - loss: 2.2110 - val_accuracy: 0.2008 - val_loss: 3.6071 Epoch 30/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 87s 466ms/step - accuracy: 0.4175 - loss: 2.1086 - val_accuracy: 0.2302 - val_loss: 3.4161 Epoch 31/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 87s 465ms/step - accuracy: 0.4359 - loss: 2.0610 - val_accuracy: 0.2231 - val_loss: 3.5957 Epoch 32/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 148s 498ms/step - accuracy: 0.4463 - loss: 1.9866 - val_accuracy: 0.2234 - val_loss: 3.7263 Epoch 33/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 92s 489ms/step - accuracy: 0.4613 - loss: 1.8821 - val_accuracy: 0.2239 - val_loss: 3.6929 Epoch 34/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 139s 475ms/step - accuracy: 0.4925 - loss: 1.7858 - val_accuracy: 0.2238 - val_loss: 3.8351 Epoch 35/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 91s 485ms/step - accuracy: 0.5105 - loss: 1.7074 - val_accuracy: 0.1930 - val_loss: 4.1941 Epoch 36/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 140s 474ms/step - accuracy: 0.5334 - loss: 1.6256 - val_accuracy: 0.2098 - val_loss: 4.1464 Epoch 37/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 87s 464ms/step - accuracy: 0.5504 - loss: 1.5603 - val_accuracy: 0.2306 - val_loss: 4.0215 Epoch 38/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 90s 480ms/step - accuracy: 0.5736 - loss: 1.4419 - val_accuracy: 0.2240 - val_loss: 4.1604 Epoch 39/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 91s 486ms/step - accuracy: 0.6025 - loss: 1.3612 - val_accuracy: 0.2344 - val_loss: 4.0505 Epoch 40/40 187/187 ━━━━━━━━━━━━━━━━━━━━ 89s 474ms/step - accuracy: 0.6199 - loss: 1.2889 - val_accuracy: 0.2151 - val_loss: 4.3660 </code></pre></div> </div> Training the model is relatively fast. This might make it sounds easy to simply train EfficientNet on any dataset wanted from scratch. However, training EfficientNet on smaller datasets, especially those with lower resolution like CIFAR-100, faces the significant challenge of overfitting. Hence training from scratch requires very careful choice of hyperparameters and is difficult to find suitable regularization. It would also be much more demanding in resources. Plotting the training and validation accuracy makes it clear that validation accuracy stagnates at a low value. <div class="codehilite"><pre><code>import matplotlib.pyplot as plt def 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) </code></pre></div> <img alt="png" src="/img/examples/vision/image_classification_efficientnet_fine_tuning/image_classification_efficientnet_fine_tuning_19_0.png" /> <hr /> <h2 id="transfer-learning-from-pretrained-weights">Transfer learning from pre-trained weights</h2> Here we initialize the model with pre-trained ImageNet weights, and we fine-tune it on our own dataset. <div class="codehilite"><pre><code>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 </code></pre></div> The first step to transfer learning is to freeze all layers and train only the top layers. For this step, a relatively large learning rate (1e-2) can be used. Note that validation accuracy and loss will usually be better than training accuracy and loss. This is because the regularization is strong, which only suppresses training-time metrics. Note that the convergence may take up to 50 epochs depending on choice of learning rate. If image augmentation layers were not applied, the validation accuracy may only reach ~60%. <div class="codehilite"><pre><code>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) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 1/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 108s 432ms/step - accuracy: 0.2654 - loss: 4.3710 - val_accuracy: 0.6888 - val_loss: 1.0875 Epoch 2/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 119s 412ms/step - accuracy: 0.4863 - loss: 2.0996 - val_accuracy: 0.7282 - val_loss: 0.9072 Epoch 3/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 78s 416ms/step - accuracy: 0.5422 - loss: 1.7120 - val_accuracy: 0.7411 - val_loss: 0.8574 Epoch 4/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 77s 412ms/step - accuracy: 0.5509 - loss: 1.6472 - val_accuracy: 0.7451 - val_loss: 0.8457 Epoch 5/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 81s 431ms/step - accuracy: 0.5744 - loss: 1.5373 - val_accuracy: 0.7424 - val_loss: 0.8649 Epoch 6/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 78s 417ms/step - accuracy: 0.5715 - loss: 1.5595 - val_accuracy: 0.7374 - val_loss: 0.8736 Epoch 7/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 81s 432ms/step - accuracy: 0.5802 - loss: 1.5045 - val_accuracy: 0.7430 - val_loss: 0.8675 Epoch 8/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 77s 411ms/step - accuracy: 0.5839 - loss: 1.4972 - val_accuracy: 0.7392 - val_loss: 0.8647 Epoch 9/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 77s 411ms/step - accuracy: 0.5929 - loss: 1.4699 - val_accuracy: 0.7508 - val_loss: 0.8634 Epoch 10/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 82s 437ms/step - accuracy: 0.6040 - loss: 1.4442 - val_accuracy: 0.7520 - val_loss: 0.8480 Epoch 11/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 78s 416ms/step - accuracy: 0.5972 - loss: 1.4626 - val_accuracy: 0.7379 - val_loss: 0.8879 Epoch 12/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 79s 421ms/step - accuracy: 0.5965 - loss: 1.4700 - val_accuracy: 0.7383 - val_loss: 0.9409 Epoch 13/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 82s 420ms/step - accuracy: 0.6034 - loss: 1.4533 - val_accuracy: 0.7474 - val_loss: 0.8922 Epoch 14/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 81s 435ms/step - accuracy: 0.6053 - loss: 1.4170 - val_accuracy: 0.7416 - val_loss: 0.9119 Epoch 15/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 77s 411ms/step - accuracy: 0.6059 - loss: 1.4125 - val_accuracy: 0.7406 - val_loss: 0.9205 Epoch 16/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 82s 438ms/step - accuracy: 0.5979 - loss: 1.4554 - val_accuracy: 0.7392 - val_loss: 0.9120 Epoch 17/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 77s 411ms/step - accuracy: 0.6081 - loss: 1.4089 - val_accuracy: 0.7423 - val_loss: 0.9305 Epoch 18/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 82s 436ms/step - accuracy: 0.6041 - loss: 1.4390 - val_accuracy: 0.7380 - val_loss: 0.9644 Epoch 19/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 79s 417ms/step - accuracy: 0.6018 - loss: 1.4324 - val_accuracy: 0.7439 - val_loss: 0.9129 Epoch 20/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 81s 430ms/step - accuracy: 0.6057 - loss: 1.4342 - val_accuracy: 0.7305 - val_loss: 0.9463 Epoch 21/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 77s 410ms/step - accuracy: 0.6209 - loss: 1.3824 - val_accuracy: 0.7410 - val_loss: 0.9503 Epoch 22/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 78s 419ms/step - accuracy: 0.6170 - loss: 1.4246 - val_accuracy: 0.7336 - val_loss: 0.9606 Epoch 23/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 85s 455ms/step - accuracy: 0.6153 - loss: 1.4009 - val_accuracy: 0.7334 - val_loss: 0.9520 Epoch 24/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 82s 438ms/step - accuracy: 0.6051 - loss: 1.4343 - val_accuracy: 0.7435 - val_loss: 0.9403 Epoch 25/25 187/187 ━━━━━━━━━━━━━━━━━━━━ 138s 416ms/step - accuracy: 0.6065 - loss: 1.4131 - val_accuracy: 0.7456 - val_loss: 0.9307 </code></pre></div> </div> <img alt="png" src="/img/examples/vision/image_classification_efficientnet_fine_tuning/image_classification_efficientnet_fine_tuning_23_1.png" /> The second step is to unfreeze a number of layers and fit the model using smaller learning rate. In this example we show unfreezing all layers, but depending on specific dataset it may be desireble to only unfreeze a fraction of all layers. When the feature extraction with pretrained model works good enough, this step would give a very limited gain on validation 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 different from ImageNet, this fine-tuning step can be crucial as the feature extractor also needs to be adjusted by a considerable amount. Such a situation can be demonstrated if choosing CIFAR-100 dataset instead, where fine-tuning boosts validation accuracy by about 10% to pass 80% on <code>EfficientNetB0</code>. A side note on freezing/unfreezing models: setting <code>trainable</code> of a <code>Model</code> will simultaneously set all layers belonging to the <code>Model</code> to the same <code>trainable</code> attribute. Each layer is trainable only if both the layer itself and the model containing it are trainable. Hence when we need to partially freeze/unfreeze a model, we need to make sure the <code>trainable</code> attribute of the model is set to <code>True</code>. <div class="codehilite"><pre><code>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) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 1/4 187/187 ━━━━━━━━━━━━━━━━━━━━ 111s 442ms/step - accuracy: 0.6310 - loss: 1.3425 - val_accuracy: 0.7565 - val_loss: 0.8874 Epoch 2/4 187/187 ━━━━━━━━━━━━━━━━━━━━ 77s 413ms/step - accuracy: 0.6518 - loss: 1.2755 - val_accuracy: 0.7635 - val_loss: 0.8588 Epoch 3/4 187/187 ━━━━━━━━━━━━━━━━━━━━ 82s 437ms/step - accuracy: 0.6491 - loss: 1.2426 - val_accuracy: 0.7663 - val_loss: 0.8419 Epoch 4/4 187/187 ━━━━━━━━━━━━━━━━━━━━ 79s 419ms/step - accuracy: 0.6625 - loss: 1.1775 - val_accuracy: 0.7701 - val_loss: 0.8284 </code></pre></div> </div> <img alt="png" src="/img/examples/vision/image_classification_efficientnet_fine_tuning/image_classification_efficientnet_fine_tuning_25_1.png" /> <h3 id="tips-for-fine-tuning-efficientnet">Tips for fine tuning EfficientNet</h3> On unfreezing layers: <ul> <li>The <code>BatchNormalization</code> layers need to be kept frozen (<a href="https://keras.io/guides/transfer_learning/">more details</a>). If they are also turned to trainable, the first epoch after unfreezing will significantly reduce accuracy.</li> <li>In some cases it may be beneficial to open up only a portion of layers instead of unfreezing all. This will make fine tuning much faster when going to larger models like B7.</li> <li>Each block needs to be all turned on or off. This is because the architecture includes a shortcut from the first layer to the last layer for each block. Not respecting blocks also significantly harms the final performance.</li> </ul> Some other tips for utilizing EfficientNet: <ul> <li>Larger variants of EfficientNet do not guarantee improved performance, especially for tasks with less data or fewer classes. In such a case, the larger variant of EfficientNet chosen, the harder it is to tune hyperparameters.</li> <li>EMA (Exponential Moving Average) is very helpful in training EfficientNet from scratch, but not so much for transfer learning.</li> <li>Do not use the RMSprop setup as in the original paper for transfer learning. The momentum and learning rate are too high for transfer learning. It will easily corrupt the pretrained weight and blow up the loss. A quick check is to see if loss (as categorical cross entropy) is getting significantly larger than log(NUM_CLASSES) after the same epoch. If so, the initial learning rate/momentum is too high.</li> <li>Smaller batch size benefit validation accuracy, possibly due to effectively providing regularization.</li> </ul> </div> <div class='k-outline'> <div class='k-outline-depth-1'> <a href='#image-classification-via-finetuning-with-efficientnet'>Image classification via fine-tuning with EfficientNet</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#introduction-what-is-efficientnet'>Introduction: what is EfficientNet</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#b0-to-b7-variants-of-efficientnet'>B0 to B7 variants of EfficientNet</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#keras-implementation-of-efficientnet'>Keras implementation of EfficientNet</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#example-efficientnetb0-for-stanford-dogs'>Example: EfficientNetB0 for Stanford Dogs.</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#setup-and-data-loading'>Setup and data loading</a> </div> <div class='k-outline-depth-3'> <a href='#loading-data'>Loading data</a> </div> <div class='k-outline-depth-3'> <a href='#visualizing-the-data'>Visualizing the data</a> </div> <div class='k-outline-depth-3'> <a href='#data-augmentation'>Data augmentation</a> </div> <div class='k-outline-depth-3'> <a href='#prepare-inputs'>Prepare inputs</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#training-a-model-from-scratch'>Training a model from scratch</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#transfer-learning-from-pretrained-weights'>Transfer learning from pre-trained weights</a> </div> <div class='k-outline-depth-3'> <a href='#tips-for-fine-tuning-efficientnet'>Tips for fine tuning EfficientNet</a> </div> </div> </div> </div> </div> </body> <footer style="float: left; width: 100%; padding: 1em; border-top: solid 1px #bbb;"> <a href="https://policies.google.com/terms">Terms</a> | <a href="https://policies.google.com/privacy">Privacy</a> </footer> </html>