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+ + Sentinel Satellite Image Classification Project¶ +
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+ + Project Overview¶ +
++ This project focuses on the development and deployment of a + machine learning application for satellite image classification. + The goal is to automate the classification of satellite images + into predefined categories that represent different types of + land cover. +
++ Motivation¶ +
++ End Users¶ +
++ The end users of this project are environmental scientists and + urban planners. +
++ Goal of End Users¶ +
++ Their goal is to utilize automated tools to classify large + volumes of satellite imagery quickly and accurately for + environmental monitoring and urban planning purposes. +
++ Obstacle to be Solved¶ +
++ The main obstacles include the high variability and similarity + between different land cover types in satellite images and the + volume of data that requires processing. +
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+ import tensorflow as tf +tf.__version__ ++
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+ '2.16.1'+
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+ print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU'))) ++
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+ +Num GPUs Available: 1 ++
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+ + Data Collection and Augmentation¶ +
++ Images Collected¶ +
++ The dataset used in this project is the EuroSAT collection, + which consists of 30,988 satellite images derived from Sentinel + satellites. These images are categorized into ten classes + representing different types of land cover: AnnualCrop, Forest, + HerbaceousVegetation, Highway, Industrial, Pasture, + PermanentCrop, Residential, River, SeaLake. +
++ Description of Splitting Images into Classes/Labeling Images¶ +
++ The EuroSAT images come pre-labeled, which facilitates the + classification task. The dataset was split into a training set + comprising 80% of the images and a validation set comprising + 20%, ensuring a comprehensive evaluation of the model across + varied image data. +
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+ import numpy as np +import keras +from keras import layers +import matplotlib.pyplot as plt + +from tensorflow.keras.preprocessing.image import ImageDataGenerator ++
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+ def load_data(): + train_ds = tf.keras.utils.image_dataset_from_directory( + 'data', + validation_split=0.2, + subset="training", + seed=123, + image_size=(64, 64), + batch_size=32, + label_mode='categorical' + ) + + val_ds = tf.keras.utils.image_dataset_from_directory( + 'data', + validation_split=0.2, + subset="validation", + seed=123, + image_size=(64, 64), + batch_size=32, + label_mode='categorical' + ) + + return train_ds, val_ds, train_ds.class_names + +train_ds, val_ds, class_names = load_data() + +class_names = train_ds.class_names + +print(class_names) ++
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+ +Found 30988 files belonging to 10 classes. +Using 24791 files for training. +Found 30988 files belonging to 10 classes. +Using 6197 files for validation. +['AnnualCrop', 'Forest', 'HerbaceousVegetation', 'Highway', 'Industrial', 'Pasture', 'PermanentCrop', 'Residential', 'River', 'SeaLake'] ++
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+ import matplotlib.pyplot as plt + +for images, labels in train_ds.take(1): + plt.figure(figsize=(6, 6)) + plt.imshow(images[0].numpy().astype('uint8')) + plt.title(class_names[tf.argmax(labels[0])]) + plt.axis('off') + plt.show() + + print("Sample pixel values (0 to 1 range):", images[0].numpy().flatten()[0:5]) + print("Min and max pixel values:", images[0].numpy().min(), images[0].numpy().max()) ++
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+ +Sample pixel values (0 to 1 range): [180. 183. 156. 177. 186.] +Min and max pixel values: 74.0 248.0 ++
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+ +2024-05-05 01:03:10.842952: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence ++
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+ val_batches = tf.data.experimental.cardinality(val_ds) +test_ds = val_ds.take(val_batches // 5) +validation_ds = val_ds.skip(val_batches // 5) + + +print('Number of training batches:', tf.data.experimental.cardinality(train_ds).numpy()) +print('Number of validation batches:', tf.data.experimental.cardinality(validation_ds).numpy()) +print('Number of test batches:', tf.data.experimental.cardinality(test_ds).numpy()) ++
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+ +Number of training batches: 775 +Number of validation batches: 156 +Number of test batches: 38 ++
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+ import matplotlib.pyplot as plt +import numpy as np + +plt.figure(figsize=(10, 10)) +for images, labels in train_ds.take(1): + for i in range(9): + ax = plt.subplot(3, 3, i + 1) + plt.imshow(images[i].numpy().astype("uint8")) + class_index = np.argmax(labels[i]) + plt.title(class_names[class_index]) + plt.axis("off") ++
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+ +2024-05-05 01:03:10.923071: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence ++
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+ number_of_classes = len(train_ds.class_names) ++
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+ + Data Augmentation Description¶ +
++ To enhance the robustness of the model against variations in + real-world satellite images, several data augmentation + techniques were applied. These included random flips (both + horizontal and vertical), random rotations (up to 20 degrees), + random zoom (up to 20%), and random contrast adjustments. These + techniques help simulate different capture conditions and + photographic variations, aiding the model in learning more + generalized features. +
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+ import numpy as np +import matplotlib.pyplot as plt +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import layers + +data_augmentation = keras.Sequential([ + layers.RandomFlip("horizontal_and_vertical"), + layers.RandomRotation(0.2), + layers.RandomZoom(0.2), + layers.RandomContrast(0.1) +]) + +def augment_data(dataset): + return dataset.map(lambda x, y: (data_augmentation(x, training=True), y)) ++
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+ import numpy as np +import matplotlib.pyplot as plt + +for images, labels in train_ds.take(1): + plt.figure(figsize=(10, 10)) + first_image = images[0] + class_index = np.argmax(labels[0]) + class_name = class_names[class_index] + + for i in range(9): + ax = plt.subplot(3, 3, i + 1) + augmented_image = data_augmentation(np.expand_dims(first_image, 0)) + plt.imshow(augmented_image[0].numpy().astype("uint8")) + plt.title(class_name) + plt.axis("off") ++
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+ +2024-05-05 01:03:11.358116: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence ++
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+ dataset_length = tf.data.experimental.cardinality(train_ds).numpy() + +print("Length of the TensorFlow dataset:", dataset_length) ++
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+ +Length of the TensorFlow dataset: 775 ++
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+ + Model Training¶ +
++ Initial Training and Fine Tuning¶ +
++ The model's initial training utilized a pre-trained + EfficientNetB0 architecture with the top layers tailored for our + classification needs. The base model's layers were initially + frozen. Fine-tuning was later applied by unfreezing all layers + and continuing training, which refined the model's ability to + classify complex images more accurately. +
++ Comparison of Performance¶ +
++ Initially, the model achieved a validation accuracy of around + 92%. Post fine-tuning, this accuracy improved to approximately + 94%. This indicates the effectiveness of fine-tuning in + enhancing the model's capability to distinguish subtle features + in satellite images. +
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+ from tensorflow.keras.applications import EfficientNetB0 +from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau + +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import layers +from tensorflow.keras.models import Model +from tensorflow.keras.applications import EfficientNetB0 + +def create_efficientnet_model(input_shape, num_classes): + inputs = keras.Input(shape=input_shape) + + scale_layer = keras.layers.Rescaling(scale=1 / 127.5, offset=-1) + x = scale_layer(inputs) + + base_model = EfficientNetB0(include_top=False, weights="imagenet", input_tensor=inputs) + base_model.trainable = False + + + x = layers.GlobalAveragePooling2D()(base_model.output) + x = layers.Dense(512, activation='relu')(x) + x = layers.Dense(256, activation='relu')(x) + x = layers.Dropout(0.3)(x) + outputs = layers.Dense(num_classes, activation='softmax')(x) + + model = Model(inputs=inputs, outputs=outputs) + return model + +def fine_tune_model(model, train_ds, val_ds, epochs): + base_model = model.layers[1] + base_model.trainable = True + + model.compile(optimizer=keras.optimizers.Adam(1e-5), + loss='categorical_crossentropy', + metrics=['accuracy']) + + history_fine = model.fit(train_ds, epochs=epochs, validation_data=val_ds, callbacks=callbacks) + + return history_fine + +model = create_efficientnet_model((64, 64, 3), len(class_names)) + +model.compile(optimizer='adam', + loss='categorical_crossentropy', + metrics=['accuracy']) + +callbacks = [ + keras.callbacks.ModelCheckpoint('best_model.keras', save_best_only=True, monitor='val_loss', mode='min'), + keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, min_lr=0.00001), + keras.callbacks.EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True) +] ++
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+ initial_epochs = 10 +history = model.fit(train_ds, validation_data=validation_ds, epochs=initial_epochs, callbacks=callbacks) ++
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+ Epoch 1/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 56s 62ms/step - accuracy: 0.8013 - loss: 0.6025 - val_accuracy: 0.9022 - val_loss: 0.3121 - learning_rate: 0.0010 +Epoch 2/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 38s 49ms/step - accuracy: 0.8930 - loss: 0.3150 - val_accuracy: 0.9083 - val_loss: 0.2748 - learning_rate: 0.0010 +Epoch 3/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 39s 51ms/step - accuracy: 0.9116 - loss: 0.2614 - val_accuracy: 0.9187 - val_loss: 0.2372 - learning_rate: 0.0010 +Epoch 4/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 43s 55ms/step - accuracy: 0.9210 - loss: 0.2349 - val_accuracy: 0.9179 - val_loss: 0.2646 - learning_rate: 0.0010 +Epoch 5/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 39s 50ms/step - accuracy: 0.9255 - loss: 0.2102 - val_accuracy: 0.9239 - val_loss: 0.2526 - learning_rate: 0.0010 +Epoch 6/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 35s 46ms/step - accuracy: 0.9359 - loss: 0.1938 - val_accuracy: 0.9261 - val_loss: 0.2490 - learning_rate: 0.0010 +Epoch 7/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 37s 48ms/step - accuracy: 0.9340 - loss: 0.1869 - val_accuracy: 0.9209 - val_loss: 0.2702 - learning_rate: 0.0010 +Epoch 8/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 36s 47ms/step - accuracy: 0.9416 - loss: 0.1678 - val_accuracy: 0.9231 - val_loss: 0.2535 - learning_rate: 0.0010 +Epoch 9/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 37s 48ms/step - accuracy: 0.9444 - loss: 0.1588 - val_accuracy: 0.9219 - val_loss: 0.2727 - learning_rate: 0.0010 +Epoch 10/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 39s 50ms/step - accuracy: 0.9485 - loss: 0.1468 - val_accuracy: 0.9171 - val_loss: 0.2794 - learning_rate: 0.0010 ++
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+ epochs = 10 +history_fine = fine_tune_model(model, train_ds, validation_ds, epochs) ++
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+ Epoch 1/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 55s 62ms/step - accuracy: 0.9292 - loss: 0.2041 - val_accuracy: 0.9219 - val_loss: 0.2278 - learning_rate: 1.0000e-05 +Epoch 2/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 40s 51ms/step - accuracy: 0.9348 - loss: 0.1898 - val_accuracy: 0.9249 - val_loss: 0.2211 - learning_rate: 1.0000e-05 +Epoch 3/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 43s 55ms/step - accuracy: 0.9367 - loss: 0.1840 - val_accuracy: 0.9287 - val_loss: 0.2178 - learning_rate: 1.0000e-05 +Epoch 4/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 41s 53ms/step - accuracy: 0.9371 - loss: 0.1831 - val_accuracy: 0.9283 - val_loss: 0.2168 - learning_rate: 1.0000e-05 +Epoch 5/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 39s 51ms/step - accuracy: 0.9410 - loss: 0.1729 - val_accuracy: 0.9291 - val_loss: 0.2138 - learning_rate: 1.0000e-05 +Epoch 6/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 40s 51ms/step - accuracy: 0.9419 - loss: 0.1698 - val_accuracy: 0.9299 - val_loss: 0.2147 - learning_rate: 1.0000e-05 +Epoch 7/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 39s 50ms/step - accuracy: 0.9427 - loss: 0.1690 - val_accuracy: 0.9299 - val_loss: 0.2139 - learning_rate: 1.0000e-05 +Epoch 8/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 39s 50ms/step - accuracy: 0.9438 - loss: 0.1659 - val_accuracy: 0.9307 - val_loss: 0.2151 - learning_rate: 1.0000e-05 +Epoch 9/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 39s 50ms/step - accuracy: 0.9399 - loss: 0.1711 - val_accuracy: 0.9315 - val_loss: 0.2115 - learning_rate: 1.0000e-05 +Epoch 10/10 +775/775 ━━━━━━━━━━━━━━━━━━━━ 38s 49ms/step - accuracy: 0.9461 - loss: 0.1608 - val_accuracy: 0.9327 - val_loss: 0.2127 - learning_rate: 1.0000e-05 ++
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+ acc = history.history['accuracy'] +val_acc = history.history['val_accuracy'] + +loss = history.history['loss'] +val_loss = history.history['val_loss'] + +plt.figure(figsize=(8, 8)) +plt.subplot(2, 1, 1) +plt.plot(acc, label='Training Accuracy') +plt.plot(val_acc, label='Validation Accuracy') +plt.legend(loc='lower right') +plt.ylabel('Accuracy') +plt.ylim([min(plt.ylim()),1]) +plt.title('Training and Validation Accuracy') + +plt.subplot(2, 1, 2) +plt.plot(loss, label='Training Loss') +plt.plot(val_loss, label='Validation Loss') +plt.legend(loc='upper right') +plt.ylabel('Cross Entropy') +#plt.ylim([0,1.0]) +plt.title('Training and Validation Loss') +plt.xlabel('epoch') +plt.show() ++
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+ acc += history_fine.history['accuracy'] +val_acc += history_fine.history['val_accuracy'] + +loss += history_fine.history['loss'] +val_loss += history_fine.history['val_loss'] + +plt.figure(figsize=(8, 8)) +plt.subplot(2, 1, 1) +plt.plot(acc, label='Training Accuracy') +plt.plot(val_acc, label='Validation Accuracy') +plt.ylim([0.4, 1]) # set the y-axis limits +plt.plot([initial_epochs-1,initial_epochs-1], +plt.ylim(), label='Start Fine Tuning') +plt.legend(loc='lower right') +plt.title('Training and Validation Accuracy') + +plt.subplot(2, 1, 2) +plt.plot(loss, label='Training Loss') +plt.plot(val_loss, label='Validation Loss') +plt.plot([initial_epochs-1,initial_epochs-1], +plt.ylim(), label='Start Fine Tuning') +plt.legend(loc='upper right') +plt.title('Training and Validation Loss') +plt.xlabel('epoch') +plt.show() ++
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+ print("Test dataset evaluation") +model.evaluate(test_ds) ++
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+ Test dataset evaluation +38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 34ms/step - accuracy: 0.9317 - loss: 0.2277 ++
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+ [0.19601286947727203, 0.9358552694320679]+
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+ print(model.summary()) ++
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+ Model: "functional_15"
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+ ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ +┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃ +┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ +│ input_layer_11 │ (None, 64, 64, 3) │ 0 │ - │ +│ (InputLayer) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ rescaling_16 │ (None, 64, 64, 3) │ 0 │ input_layer_11[0… │ +│ (Rescaling) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ normalization_5 │ (None, 64, 64, 3) │ 7 │ rescaling_16[0][… │ +│ (Normalization) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ rescaling_17 │ (None, 64, 64, 3) │ 0 │ normalization_5[… │ +│ (Rescaling) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ stem_conv_pad │ (None, 65, 65, 3) │ 0 │ rescaling_17[0][… │ +│ (ZeroPadding2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ stem_conv (Conv2D) │ (None, 32, 32, │ 864 │ stem_conv_pad[0]… │ +│ │ 32) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ stem_bn │ (None, 32, 32, │ 128 │ stem_conv[0][0] │ +│ (BatchNormalizatio… │ 32) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ stem_activation │ (None, 32, 32, │ 0 │ stem_bn[0][0] │ +│ (Activation) │ 32) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block1a_dwconv │ (None, 32, 32, │ 288 │ stem_activation[… │ +│ (DepthwiseConv2D) │ 32) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block1a_bn │ (None, 32, 32, │ 128 │ block1a_dwconv[0… │ +│ (BatchNormalizatio… │ 32) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block1a_activation │ (None, 32, 32, │ 0 │ block1a_bn[0][0] │ +│ (Activation) │ 32) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block1a_se_squeeze │ (None, 32) │ 0 │ block1a_activati… │ +│ (GlobalAveragePool… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block1a_se_reshape │ (None, 1, 1, 32) │ 0 │ block1a_se_squee… │ +│ (Reshape) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block1a_se_reduce │ (None, 1, 1, 8) │ 264 │ block1a_se_resha… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block1a_se_expand │ (None, 1, 1, 32) │ 288 │ block1a_se_reduc… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block1a_se_excite │ (None, 32, 32, │ 0 │ block1a_activati… │ +│ (Multiply) │ 32) │ │ block1a_se_expan… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block1a_project_co… │ (None, 32, 32, │ 512 │ block1a_se_excit… │ +│ (Conv2D) │ 16) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block1a_project_bn │ (None, 32, 32, │ 64 │ block1a_project_… │ +│ (BatchNormalizatio… │ 16) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2a_expand_conv │ (None, 32, 32, │ 1,536 │ block1a_project_… │ +│ (Conv2D) │ 96) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2a_expand_bn │ (None, 32, 32, │ 384 │ block2a_expand_c… │ +│ (BatchNormalizatio… │ 96) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2a_expand_act… │ (None, 32, 32, │ 0 │ block2a_expand_b… │ +│ (Activation) │ 96) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2a_dwconv_pad │ (None, 33, 33, │ 0 │ block2a_expand_a… │ +│ (ZeroPadding2D) │ 96) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2a_dwconv │ (None, 16, 16, │ 864 │ block2a_dwconv_p… │ +│ (DepthwiseConv2D) │ 96) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2a_bn │ (None, 16, 16, │ 384 │ block2a_dwconv[0… │ +│ (BatchNormalizatio… │ 96) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2a_activation │ (None, 16, 16, │ 0 │ block2a_bn[0][0] │ +│ (Activation) │ 96) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2a_se_squeeze │ (None, 96) │ 0 │ block2a_activati… │ +│ (GlobalAveragePool… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2a_se_reshape │ (None, 1, 1, 96) │ 0 │ block2a_se_squee… │ +│ (Reshape) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2a_se_reduce │ (None, 1, 1, 4) │ 388 │ block2a_se_resha… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2a_se_expand │ (None, 1, 1, 96) │ 480 │ block2a_se_reduc… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2a_se_excite │ (None, 16, 16, │ 0 │ block2a_activati… │ +│ (Multiply) │ 96) │ │ block2a_se_expan… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2a_project_co… │ (None, 16, 16, │ 2,304 │ block2a_se_excit… │ +│ (Conv2D) │ 24) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2a_project_bn │ (None, 16, 16, │ 96 │ block2a_project_… │ +│ (BatchNormalizatio… │ 24) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2b_expand_conv │ (None, 16, 16, │ 3,456 │ block2a_project_… │ +│ (Conv2D) │ 144) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2b_expand_bn │ (None, 16, 16, │ 576 │ block2b_expand_c… │ +│ (BatchNormalizatio… │ 144) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2b_expand_act… │ (None, 16, 16, │ 0 │ block2b_expand_b… │ +│ (Activation) │ 144) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2b_dwconv │ (None, 16, 16, │ 1,296 │ block2b_expand_a… │ +│ (DepthwiseConv2D) │ 144) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2b_bn │ (None, 16, 16, │ 576 │ block2b_dwconv[0… │ +│ (BatchNormalizatio… │ 144) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2b_activation │ (None, 16, 16, │ 0 │ block2b_bn[0][0] │ +│ (Activation) │ 144) │ │ │ +├─────────────────────┼───────────���───────┼────────────┼───────────────────┤ +│ block2b_se_squeeze │ (None, 144) │ 0 │ block2b_activati… │ +│ (GlobalAveragePool… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2b_se_reshape │ (None, 1, 1, 144) │ 0 │ block2b_se_squee… │ +│ (Reshape) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2b_se_reduce │ (None, 1, 1, 6) │ 870 │ block2b_se_resha… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2b_se_expand │ (None, 1, 1, 144) │ 1,008 │ block2b_se_reduc… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2b_se_excite │ (None, 16, 16, │ 0 │ block2b_activati… │ +│ (Multiply) │ 144) │ │ block2b_se_expan… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2b_project_co… │ (None, 16, 16, │ 3,456 │ block2b_se_excit… │ +│ (Conv2D) │ 24) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2b_project_bn │ (None, 16, 16, │ 96 │ block2b_project_… │ +│ (BatchNormalizatio… │ 24) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2b_drop │ (None, 16, 16, │ 0 │ block2b_project_… │ +│ (Dropout) │ 24) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block2b_add (Add) │ (None, 16, 16, │ 0 │ block2b_drop[0][… │ +│ │ 24) │ │ block2a_project_… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3a_expand_conv │ (None, 16, 16, │ 3,456 │ block2b_add[0][0] │ +│ (Conv2D) │ 144) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3a_expand_bn │ (None, 16, 16, │ 576 │ block3a_expand_c… │ +│ (BatchNormalizatio… │ 144) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3a_expand_act… │ (None, 16, 16, │ 0 │ block3a_expand_b… │ +│ (Activation) │ 144) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3a_dwconv_pad │ (None, 19, 19, │ 0 │ block3a_expand_a… │ +│ (ZeroPadding2D) │ 144) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3a_dwconv │ (None, 8, 8, 144) │ 3,600 │ block3a_dwconv_p… │ +│ (DepthwiseConv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3a_bn │ (None, 8, 8, 144) │ 576 │ block3a_dwconv[0… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3a_activation │ (None, 8, 8, 144) │ 0 │ block3a_bn[0][0] │ +│ (Activation) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3a_se_squeeze │ (None, 144) │ 0 │ block3a_activati… │ +│ (GlobalAveragePool… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3a_se_reshape │ (None, 1, 1, 144) │ 0 │ block3a_se_squee… │ +│ (Reshape) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3a_se_reduce │ (None, 1, 1, 6) │ 870 │ block3a_se_resha… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3a_se_expand │ (None, 1, 1, 144) │ 1,008 │ block3a_se_reduc… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3a_se_excite │ (None, 8, 8, 144) │ 0 │ block3a_activati… │ +│ (Multiply) │ │ │ block3a_se_expan… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3a_project_co… │ (None, 8, 8, 40) │ 5,760 │ block3a_se_excit… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3a_project_bn │ (None, 8, 8, 40) │ 160 │ block3a_project_… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3b_expand_conv │ (None, 8, 8, 240) │ 9,600 │ block3a_project_… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3b_expand_bn │ (None, 8, 8, 240) │ 960 │ block3b_expand_c… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3b_expand_act… │ (None, 8, 8, 240) │ 0 │ block3b_expand_b… │ +│ (Activation) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3b_dwconv │ (None, 8, 8, 240) │ 6,000 │ block3b_expand_a… │ +│ (DepthwiseConv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3b_bn │ (None, 8, 8, 240) │ 960 │ block3b_dwconv[0… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3b_activation │ (None, 8, 8, 240) │ 0 │ block3b_bn[0][0] │ +│ (Activation) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3b_se_squeeze │ (None, 240) │ 0 │ block3b_activati… │ +│ (GlobalAveragePool… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3b_se_reshape │ (None, 1, 1, 240) │ 0 │ block3b_se_squee… │ +│ (Reshape) │ │ │ │ +├─────────────────────┼─────────────���─────┼────────────┼───────────────────┤ +│ block3b_se_reduce │ (None, 1, 1, 10) │ 2,410 │ block3b_se_resha… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3b_se_expand │ (None, 1, 1, 240) │ 2,640 │ block3b_se_reduc… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3b_se_excite │ (None, 8, 8, 240) │ 0 │ block3b_activati… │ +│ (Multiply) │ │ │ block3b_se_expan… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3b_project_co… │ (None, 8, 8, 40) │ 9,600 │ block3b_se_excit… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3b_project_bn │ (None, 8, 8, 40) │ 160 │ block3b_project_… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3b_drop │ (None, 8, 8, 40) │ 0 │ block3b_project_… │ +│ (Dropout) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block3b_add (Add) │ (None, 8, 8, 40) │ 0 │ block3b_drop[0][… │ +│ │ │ │ block3a_project_… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4a_expand_conv │ (None, 8, 8, 240) │ 9,600 │ block3b_add[0][0] │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4a_expand_bn │ (None, 8, 8, 240) │ 960 │ block4a_expand_c… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4a_expand_act… │ (None, 8, 8, 240) │ 0 │ block4a_expand_b… │ +│ (Activation) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4a_dwconv_pad │ (None, 9, 9, 240) │ 0 │ block4a_expand_a… │ +│ (ZeroPadding2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4a_dwconv │ (None, 4, 4, 240) │ 2,160 │ block4a_dwconv_p… │ +│ (DepthwiseConv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4a_bn │ (None, 4, 4, 240) │ 960 │ block4a_dwconv[0… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4a_activation │ (None, 4, 4, 240) │ 0 │ block4a_bn[0][0] │ +│ (Activation) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4a_se_squeeze │ (None, 240) │ 0 │ block4a_activati… │ +│ (GlobalAveragePool… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4a_se_reshape │ (None, 1, 1, 240) │ 0 │ block4a_se_squee… │ +│ (Reshape) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4a_se_reduce │ (None, 1, 1, 10) │ 2,410 │ block4a_se_resha… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4a_se_expand │ (None, 1, 1, 240) │ 2,640 │ block4a_se_reduc… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4a_se_excite │ (None, 4, 4, 240) │ 0 │ block4a_activati… │ +│ (Multiply) │ │ │ block4a_se_expan… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4a_project_co… │ (None, 4, 4, 80) │ 19,200 │ block4a_se_excit… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4a_project_bn │ (None, 4, 4, 80) │ 320 │ block4a_project_… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4b_expand_conv │ (None, 4, 4, 480) │ 38,400 │ block4a_project_… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4b_expand_bn │ (None, 4, 4, 480) │ 1,920 │ block4b_expand_c… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4b_expand_act… │ (None, 4, 4, 480) │ 0 │ block4b_expand_b… │ +│ (Activation) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4b_dwconv │ (None, 4, 4, 480) │ 4,320 │ block4b_expand_a… │ +│ (DepthwiseConv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4b_bn │ (None, 4, 4, 480) │ 1,920 │ block4b_dwconv[0… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4b_activation │ (None, 4, 4, 480) │ 0 │ block4b_bn[0][0] │ +│ (Activation) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4b_se_squeeze │ (None, 480) │ 0 │ block4b_activati… │ +│ (GlobalAveragePool… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4b_se_reshape │ (None, 1, 1, 480) │ 0 │ block4b_se_squee… │ +│ (Reshape) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4b_se_reduce │ (None, 1, 1, 20) │ 9,620 │ block4b_se_resha… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4b_se_expand │ (None, 1, 1, 480) │ 10,080 │ block4b_se_reduc… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4b_se_excite │ (None, 4, 4, 480) │ 0 │ block4b_activati… │ +│ (Multiply) │ │ │ block4b_se_expan… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4b_project_co… │ (None, 4, 4, 80) │ 38,400 │ block4b_se_excit… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4b_project_bn │ (None, 4, 4, 80) │ 320 │ block4b_project_… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4b_drop │ (None, 4, 4, 80) │ 0 │ block4b_project_… │ +│ (Dropout) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4b_add (Add) │ (None, 4, 4, 80) │ 0 │ block4b_drop[0][… │ +│ │ │ │ block4a_project_… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4c_expand_conv │ (None, 4, 4, 480) │ 38,400 │ block4b_add[0][0] │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4c_expand_bn │ (None, 4, 4, 480) │ 1,920 │ block4c_expand_c… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4c_expand_act… │ (None, 4, 4, 480) │ 0 │ block4c_expand_b… │ +│ (Activation) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4c_dwconv │ (None, 4, 4, 480) │ 4,320 │ block4c_expand_a… │ +│ (DepthwiseConv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4c_bn │ (None, 4, 4, 480) │ 1,920 │ block4c_dwconv[0… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4c_activation │ (None, 4, 4, 480) │ 0 │ block4c_bn[0][0] │ +│ (Activation) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4c_se_squeeze │ (None, 480) │ 0 │ block4c_activati… │ +│ (GlobalAveragePool… │ │ │ │ +├─────────────────────┼──��────────────────┼────────────┼───────────────────┤ +│ block4c_se_reshape │ (None, 1, 1, 480) │ 0 │ block4c_se_squee… │ +│ (Reshape) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4c_se_reduce │ (None, 1, 1, 20) │ 9,620 │ block4c_se_resha… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4c_se_expand │ (None, 1, 1, 480) │ 10,080 │ block4c_se_reduc… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4c_se_excite │ (None, 4, 4, 480) │ 0 │ block4c_activati… │ +│ (Multiply) │ │ │ block4c_se_expan… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4c_project_co… │ (None, 4, 4, 80) │ 38,400 │ block4c_se_excit… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4c_project_bn │ (None, 4, 4, 80) │ 320 │ block4c_project_… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4c_drop │ (None, 4, 4, 80) │ 0 │ block4c_project_… │ +│ (Dropout) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block4c_add (Add) │ (None, 4, 4, 80) │ 0 │ block4c_drop[0][… │ +│ │ │ │ block4b_add[0][0] │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5a_expand_conv │ (None, 4, 4, 480) │ 38,400 │ block4c_add[0][0] │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5a_expand_bn │ (None, 4, 4, 480) │ 1,920 │ block5a_expand_c… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5a_expand_act… │ (None, 4, 4, 480) │ 0 │ block5a_expand_b… │ +│ (Activation) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5a_dwconv │ (None, 4, 4, 480) │ 12,000 │ block5a_expand_a… │ +│ (DepthwiseConv2D) │ │ │ │ +├─────────────────────┼─────────────���─────┼────────────┼───────────────────┤ +│ block5a_bn │ (None, 4, 4, 480) │ 1,920 │ block5a_dwconv[0… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5a_activation │ (None, 4, 4, 480) │ 0 │ block5a_bn[0][0] │ +│ (Activation) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5a_se_squeeze │ (None, 480) │ 0 │ block5a_activati… │ +│ (GlobalAveragePool… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5a_se_reshape │ (None, 1, 1, 480) │ 0 │ block5a_se_squee… │ +│ (Reshape) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5a_se_reduce │ (None, 1, 1, 20) │ 9,620 │ block5a_se_resha… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5a_se_expand │ (None, 1, 1, 480) │ 10,080 │ block5a_se_reduc… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5a_se_excite │ (None, 4, 4, 480) │ 0 │ block5a_activati… │ +│ (Multiply) │ │ │ block5a_se_expan… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5a_project_co… │ (None, 4, 4, 112) │ 53,760 │ block5a_se_excit… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5a_project_bn │ (None, 4, 4, 112) │ 448 │ block5a_project_… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5b_expand_conv │ (None, 4, 4, 672) │ 75,264 │ block5a_project_… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5b_expand_bn │ (None, 4, 4, 672) │ 2,688 │ block5b_expand_c… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5b_expand_act… │ (None, 4, 4, 672) │ 0 │ block5b_expand_b… │ +│ (Activation) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5b_dwconv │ (None, 4, 4, 672) │ 16,800 │ block5b_expand_a… │ +│ (DepthwiseConv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5b_bn │ (None, 4, 4, 672) │ 2,688 │ block5b_dwconv[0… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5b_activation │ (None, 4, 4, 672) │ 0 │ block5b_bn[0][0] │ +│ (Activation) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5b_se_squeeze │ (None, 672) │ 0 │ block5b_activati… │ +│ (GlobalAveragePool… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5b_se_reshape │ (None, 1, 1, 672) │ 0 │ block5b_se_squee… │ +│ (Reshape) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5b_se_reduce │ (None, 1, 1, 28) │ 18,844 │ block5b_se_resha… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5b_se_expand │ (None, 1, 1, 672) │ 19,488 │ block5b_se_reduc… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5b_se_excite │ (None, 4, 4, 672) │ 0 │ block5b_activati… │ +│ (Multiply) │ │ │ block5b_se_expan… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5b_project_co… │ (None, 4, 4, 112) │ 75,264 │ block5b_se_excit… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5b_project_bn │ (None, 4, 4, 112) │ 448 │ block5b_project_… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5b_drop │ (None, 4, 4, 112) │ 0 │ block5b_project_… │ +│ (Dropout) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5b_add (Add) │ (None, 4, 4, 112) │ 0 │ block5b_drop[0][… │ +│ │ │ │ block5a_project_… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5c_expand_conv │ (None, 4, 4, 672) │ 75,264 │ block5b_add[0][0] │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5c_expand_bn │ (None, 4, 4, 672) │ 2,688 │ block5c_expand_c… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5c_expand_act… │ (None, 4, 4, 672) │ 0 │ block5c_expand_b… │ +│ (Activation) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5c_dwconv │ (None, 4, 4, 672) │ 16,800 │ block5c_expand_a… │ +│ (DepthwiseConv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5c_bn │ (None, 4, 4, 672) │ 2,688 │ block5c_dwconv[0… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5c_activation │ (None, 4, 4, 672) │ 0 │ block5c_bn[0][0] │ +│ (Activation) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5c_se_squeeze │ (None, 672) │ 0 │ block5c_activati… │ +│ (GlobalAveragePool… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5c_se_reshape │ (None, 1, 1, 672) │ 0 │ block5c_se_squee… │ +│ (Reshape) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5c_se_reduce │ (None, 1, 1, 28) │ 18,844 │ block5c_se_resha… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5c_se_expand │ (None, 1, 1, 672) │ 19,488 │ block5c_se_reduc… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5c_se_excite │ (None, 4, 4, 672) │ 0 │ block5c_activati… │ +│ (Multiply) │ │ │ block5c_se_expan… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5c_project_co… │ (None, 4, 4, 112) │ 75,264 │ block5c_se_excit… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5c_project_bn │ (None, 4, 4, 112) │ 448 │ block5c_project_… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5c_drop │ (None, 4, 4, 112) │ 0 │ block5c_project_… │ +│ (Dropout) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block5c_add (Add) │ (None, 4, 4, 112) │ 0 │ block5c_drop[0][… │ +│ │ │ │ block5b_add[0][0] │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6a_expand_conv │ (None, 4, 4, 672) │ 75,264 │ block5c_add[0][0] │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6a_expand_bn │ (None, 4, 4, 672) │ 2,688 │ block6a_expand_c… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6a_expand_act… │ (None, 4, 4, 672) │ 0 │ block6a_expand_b… │ +│ (Activation) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6a_dwconv_pad │ (None, 7, 7, 672) │ 0 │ block6a_expand_a… │ +│ (ZeroPadding2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6a_dwconv │ (None, 2, 2, 672) │ 16,800 │ block6a_dwconv_p… │ +│ (DepthwiseConv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6a_bn │ (None, 2, 2, 672) │ 2,688 │ block6a_dwconv[0… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6a_activation │ (None, 2, 2, 672) │ 0 │ block6a_bn[0][0] │ +│ (Activation) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6a_se_squeeze │ (None, 672) │ 0 │ block6a_activati… │ +│ (GlobalAveragePool… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6a_se_reshape │ (None, 1, 1, 672) │ 0 │ block6a_se_squee… │ +│ (Reshape) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6a_se_reduce │ (None, 1, 1, 28) │ 18,844 │ block6a_se_resha… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼─────────���─────────┼────────────┼───────────────────┤ +│ block6a_se_expand │ (None, 1, 1, 672) │ 19,488 │ block6a_se_reduc… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6a_se_excite │ (None, 2, 2, 672) │ 0 │ block6a_activati… │ +│ (Multiply) │ │ │ block6a_se_expan… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6a_project_co… │ (None, 2, 2, 192) │ 129,024 │ block6a_se_excit… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6a_project_bn │ (None, 2, 2, 192) │ 768 │ block6a_project_… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6b_expand_conv │ (None, 2, 2, │ 221,184 │ block6a_project_… │ +│ (Conv2D) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6b_expand_bn │ (None, 2, 2, │ 4,608 │ block6b_expand_c… │ +│ (BatchNormalizatio… │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6b_expand_act… │ (None, 2, 2, │ 0 │ block6b_expand_b… │ +│ (Activation) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6b_dwconv │ (None, 2, 2, │ 28,800 │ block6b_expand_a… │ +│ (DepthwiseConv2D) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6b_bn │ (None, 2, 2, │ 4,608 │ block6b_dwconv[0… │ +│ (BatchNormalizatio… │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6b_activation │ (None, 2, 2, │ 0 │ block6b_bn[0][0] │ +│ (Activation) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6b_se_squeeze │ (None, 1152) │ 0 │ block6b_activati… │ +│ (GlobalAveragePool… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6b_se_reshape │ (None, 1, 1, │ 0 │ block6b_se_squee… │ +│ (Reshape) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6b_se_reduce │ (None, 1, 1, 48) │ 55,344 │ block6b_se_resha… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6b_se_expand │ (None, 1, 1, │ 56,448 │ block6b_se_reduc… │ +│ (Conv2D) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6b_se_excite │ (None, 2, 2, │ 0 │ block6b_activati… │ +│ (Multiply) │ 1152) │ │ block6b_se_expan… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6b_project_co… │ (None, 2, 2, 192) │ 221,184 │ block6b_se_excit… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6b_project_bn │ (None, 2, 2, 192) │ 768 │ block6b_project_… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6b_drop │ (None, 2, 2, 192) │ 0 │ block6b_project_… │ +│ (Dropout) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6b_add (Add) │ (None, 2, 2, 192) │ 0 │ block6b_drop[0][… │ +│ │ │ │ block6a_project_… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6c_expand_conv │ (None, 2, 2, │ 221,184 │ block6b_add[0][0] │ +│ (Conv2D) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6c_expand_bn │ (None, 2, 2, │ 4,608 │ block6c_expand_c… │ +│ (BatchNormalizatio… │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6c_expand_act… │ (None, 2, 2, │ 0 │ block6c_expand_b… │ +│ (Activation) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6c_dwconv │ (None, 2, 2, │ 28,800 │ block6c_expand_a… │ +│ (DepthwiseConv2D) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6c_bn │ (None, 2, 2, │ 4,608 │ block6c_dwconv[0… │ +│ (BatchNormalizatio… │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6c_activation │ (None, 2, 2, │ 0 │ block6c_bn[0][0] │ +│ (Activation) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6c_se_squeeze │ (None, 1152) │ 0 │ block6c_activati… │ +│ (GlobalAveragePool… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6c_se_reshape │ (None, 1, 1, │ 0 │ block6c_se_squee… │ +│ (Reshape) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6c_se_reduce │ (None, 1, 1, 48) │ 55,344 │ block6c_se_resha… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6c_se_expand │ (None, 1, 1, │ 56,448 │ block6c_se_reduc… │ +│ (Conv2D) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6c_se_excite │ (None, 2, 2, │ 0 │ block6c_activati… │ +│ (Multiply) │ 1152) │ │ block6c_se_expan… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6c_project_co… │ (None, 2, 2, 192) │ 221,184 │ block6c_se_excit… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6c_project_bn │ (None, 2, 2, 192) │ 768 │ block6c_project_… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6c_drop │ (None, 2, 2, 192) │ 0 │ block6c_project_… │ +│ (Dropout) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6c_add (Add) │ (None, 2, 2, 192) │ 0 │ block6c_drop[0][… │ +│ │ │ │ block6b_add[0][0] │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6d_expand_conv │ (None, 2, 2, │ 221,184 │ block6c_add[0][0] │ +│ (Conv2D) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6d_expand_bn │ (None, 2, 2, │ 4,608 │ block6d_expand_c… │ +│ (BatchNormalizatio… │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6d_expand_act… │ (None, 2, 2, │ 0 │ block6d_expand_b… │ +��� (Activation) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6d_dwconv │ (None, 2, 2, │ 28,800 │ block6d_expand_a… │ +│ (DepthwiseConv2D) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6d_bn │ (None, 2, 2, │ 4,608 │ block6d_dwconv[0… │ +│ (BatchNormalizatio… │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6d_activation │ (None, 2, 2, │ 0 │ block6d_bn[0][0] │ +│ (Activation) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6d_se_squeeze │ (None, 1152) │ 0 │ block6d_activati… │ +│ (GlobalAveragePool… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6d_se_reshape │ (None, 1, 1, │ 0 │ block6d_se_squee… │ +│ (Reshape) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6d_se_reduce │ (None, 1, 1, 48) │ 55,344 │ block6d_se_resha… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6d_se_expand │ (None, 1, 1, │ 56,448 │ block6d_se_reduc… │ +│ (Conv2D) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6d_se_excite │ (None, 2, 2, │ 0 │ block6d_activati… │ +│ (Multiply) │ 1152) │ │ block6d_se_expan… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6d_project_co… │ (None, 2, 2, 192) │ 221,184 │ block6d_se_excit… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6d_project_bn │ (None, 2, 2, 192) │ 768 │ block6d_project_… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6d_drop │ (None, 2, 2, 192) │ 0 │ block6d_project_… │ +│ (Dropout) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block6d_add (Add) │ (None, 2, 2, 192) │ 0 │ block6d_drop[0][… │ +│ │ │ │ block6c_add[0][0] │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block7a_expand_conv │ (None, 2, 2, │ 221,184 │ block6d_add[0][0] │ +│ (Conv2D) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block7a_expand_bn │ (None, 2, 2, │ 4,608 │ block7a_expand_c… │ +│ (BatchNormalizatio… │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block7a_expand_act… │ (None, 2, 2, │ 0 │ block7a_expand_b… │ +│ (Activation) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block7a_dwconv │ (None, 2, 2, │ 10,368 │ block7a_expand_a… │ +│ (DepthwiseConv2D) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block7a_bn │ (None, 2, 2, │ 4,608 │ block7a_dwconv[0… │ +│ (BatchNormalizatio… │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block7a_activation │ (None, 2, 2, │ 0 │ block7a_bn[0][0] │ +│ (Activation) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block7a_se_squeeze │ (None, 1152) │ 0 │ block7a_activati… │ +│ (GlobalAveragePool… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block7a_se_reshape │ (None, 1, 1, │ 0 │ block7a_se_squee… │ +│ (Reshape) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block7a_se_reduce │ (None, 1, 1, 48) │ 55,344 │ block7a_se_resha… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block7a_se_expand │ (None, 1, 1, │ 56,448 │ block7a_se_reduc… │ +│ (Conv2D) │ 1152) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block7a_se_excite │ (None, 2, 2, │ 0 │ block7a_activati… │ +│ (Multiply) │ 1152) │ │ block7a_se_expan… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ block7a_project_co… │ (None, 2, 2, 320) │ 368,640 │ block7a_se_excit… │ +│ (Conv2D) │ │ │ │ +├─────────────────────┼──────────────��────┼────────────┼───────────────────┤ +│ block7a_project_bn │ (None, 2, 2, 320) │ 1,280 │ block7a_project_… │ +│ (BatchNormalizatio… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ top_conv (Conv2D) │ (None, 2, 2, │ 409,600 │ block7a_project_… │ +│ │ 1280) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ top_bn │ (None, 2, 2, │ 5,120 │ top_conv[0][0] │ +│ (BatchNormalizatio… │ 1280) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ top_activation │ (None, 2, 2, │ 0 │ top_bn[0][0] │ +│ (Activation) │ 1280) │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ global_average_poo… │ (None, 1280) │ 0 │ top_activation[0… │ +│ (GlobalAveragePool… │ │ │ │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ dense_15 (Dense) │ (None, 512) │ 655,872 │ global_average_p… │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ dense_16 (Dense) │ (None, 256) │ 131,328 │ dense_15[0][0] │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ dropout_4 (Dropout) │ (None, 256) │ 0 │ dense_16[0][0] │ +├─────────────────────┼───────────────────┼────────────┼───────────────────┤ +│ dense_17 (Dense) │ (None, 10) │ 2,570 │ dropout_4[0][0] │ +└─────────────────────┴───────────────────┴────────────┴───────────────────┘ ++
+
+
+
+
+ Total params: 6,418,883 (24.49 MB) ++
+
+
+
+
+ Trainable params: 789,770 (3.01 MB) ++
+
+
+
+
+ Non-trainable params: 4,049,571 (15.45 MB) ++
+
+
+
+
+ Optimizer params: 1,579,542 (6.03 MB) ++
+
+
+
+
+ +None ++
+
+
+
+
+
+
+ In [ ]:
+
+
+
+
+
+
+ print("Test dataset evaluation") +model.evaluate(test_ds) ++
+
+
+
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+ Test dataset evaluation +38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 33ms/step - accuracy: 0.9380 - loss: 0.2024 ++
+
+ Out[ ]:
+
+
+ [0.20488472282886505, 0.9358552694320679]+
+
+
+
+
+
+
+ In [ ]:
+
+
+
+
+
+
+ import numpy as np +import tensorflow as tf + +y_true = [] +y_pred = [] + +for images, labels in test_ds: + + predictions = model.predict(images) + predicted_labels = np.argmax(predictions, axis=1) + + + if labels.ndim > 1 and labels.shape[1] > 1: + labels = np.argmax(labels, axis=1) + + y_true.extend(labels) + y_pred.extend(predicted_labels) ++
+
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+
+ 1/1 ━━━━━━━━━━━━━━━━━━━━ 5s 5s/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 84ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 32ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 26ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 26ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 26ms/step +1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step ++
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+ +2024-05-05 01:16:54.328388: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence ++
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+ In [ ]:
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+ from sklearn.metrics import confusion_matrix +import matplotlib.pyplot as plt +import seaborn as sns + +cm = confusion_matrix(y_true, y_pred)a + +plt.figure(figsize=(10, 8)) +sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=class_names, yticklabels=class_names) +plt.xlabel('Predicted Labels') +plt.ylabel('True Labels') +plt.title('Confusion Matrix') +plt.show() ++
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+ 
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+ In [ ]:
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+ model.save('sentinel_classificatiion_model_generated.keras') ++
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+ In [ ]:
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+ import matplotlib.pyplot as plt +import numpy as np +import tensorflow as tf + +class_names = ['AnnualCrop', 'Forest', 'HerbaceousVegetation', 'Highway', 'Industrial', 'Pasture', 'PermanentCrop', 'Residential', 'River', 'SeaLake'] + +def plot_images(images, labels, predictions): + plt.figure(figsize=(10, 10)) + for i in range(9): + plt.subplot(3, 3, i + 1) + plt.imshow(images[i].numpy().astype("uint8")) + plt.title(f"True: {class_names[np.argmax(labels[i])]}, Pred: {class_names[np.argmax(predictions[i])]}") + plt.axis("off") + + +for images, labels in test_ds.take(1): + predictions = model.predict(images) + plot_images(images, labels, predictions) + plt.show() ++
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+ 1/1 ━━━━━━━━━━━━━━━━━━━━ 8s 8s/step ++
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+ 
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+ +2024-05-04 23:42:16.121259: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence ++
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+ + Model Application¶ +
++ Deployment¶ +
++ The model was deployed using a Gradio web interface, which + provides a user-friendly GUI for uploading images and receiving + instant classifications. +
+Demo¶
++ A live demo of the application can be accessed at: + https://huggingface.co/spaces/Lars2000/sentinel +
++ Results of User Validation¶ +
++ User feedback highlighted the application's ease of use and + accuracy. Positive points included quick response times and + informative confidence scores for different classifications. + Suggestions for improvement were focused on enhancing + performance with low-contrast images and those affected by cloud + cover. +
++ Conclusion¶ +
++ The project successfully demonstrated the application of + convolutional neural networks in classifying satellite imagery, + utilizing both transfer learning and fine-tuning approaches to + achieve high accuracy. Future improvements could address the + challenges identified through user feedback, potentially + involving the incorporation of additional data preprocessing + steps or advanced neural network architectures. +
+