Edge Impulse Experts / Domestic LRAD activated with Computer Vision Public
Primary version

Training settings

Please provide a valid number of training cycles (numeric only)
Please provide a valid number for the learning rate (between 0 and 1)
Please provide a valid training processor option

Augmentation settings

Advanced training settings

Neural network architecture

import math, requests from pathlib import Path import tensorflow as tf from tensorflow.keras import Model from tensorflow.keras.models import Sequential from tensorflow.keras.layers import ( Dense, InputLayer, Dropout, Conv1D, Flatten, Reshape, MaxPooling1D, BatchNormalization, Conv2D, GlobalMaxPooling2D, Lambda, GlobalAveragePooling2D) from tensorflow.keras.optimizers.legacy import Adam, Adadelta from tensorflow.keras.losses import categorical_crossentropy sys.path.append('./resources/libraries') import ei_tensorflow.training WEIGHTS_PATH = './transfer-learning-weights/keras/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_0.35_96.h5' # Download the model weights root_url = 'https://cdn.edgeimpulse.com/' p = Path(WEIGHTS_PATH) if not p.exists(): print(f"Pretrained weights {WEIGHTS_PATH} unavailable; downloading...") if not p.parent.exists(): p.parent.mkdir(parents=True) weights_data = requests.get(root_url + WEIGHTS_PATH[2:]).content with open(WEIGHTS_PATH, 'wb') as f: f.write(weights_data) print(f"Pretrained weights {WEIGHTS_PATH} unavailable; downloading OK") print("") INPUT_SHAPE = (96, 96, 3) base_model = tf.keras.applications.MobileNetV2( input_shape = INPUT_SHAPE, alpha=0.35, weights = WEIGHTS_PATH ) base_model.trainable = False model = Sequential() model.add(InputLayer(input_shape=INPUT_SHAPE, name='x_input')) # Don't include the base model's top layers last_layer_index = -3 model.add(Model(inputs=base_model.inputs, outputs=base_model.layers[last_layer_index].output)) model.add(Reshape((-1, model.layers[-1].output.shape[3]))) model.add(Dense(16, activation='relu')) model.add(Dropout(0.1)) model.add(Flatten()) model.add(Dense(classes, activation='softmax')) # Implements the data augmentation policy def augment_image(image, label): # Flips the image randomly image = tf.image.random_flip_left_right(image) # Increase the image size, then randomly crop it down to # the original dimensions resize_factor = random.uniform(1, 1.2) new_height = math.floor(resize_factor * INPUT_SHAPE[0]) new_width = math.floor(resize_factor * INPUT_SHAPE[1]) image = tf.image.resize_with_crop_or_pad(image, new_height, new_width) image = tf.image.random_crop(image, size=INPUT_SHAPE) # Vary the brightness of the image image = tf.image.random_brightness(image, max_delta=0.2) return image, label train_dataset = train_dataset.map(augment_image, num_parallel_calls=tf.data.AUTOTUNE) BATCH_SIZE = args.batch_size or 32 EPOCHS = args.epochs or 20 LEARNING_RATE = args.learning_rate or 0.0005 # If True, non-deterministic functions (e.g. shuffling batches) are not used. # This is False by default. ENSURE_DETERMINISM = args.ensure_determinism if not ENSURE_DETERMINISM: train_dataset = train_dataset.shuffle(buffer_size=BATCH_SIZE*4) prefetch_policy = 1 if ENSURE_DETERMINISM else tf.data.AUTOTUNE train_dataset = train_dataset.batch(BATCH_SIZE, drop_remainder=False).prefetch(prefetch_policy) validation_dataset = validation_dataset.batch(BATCH_SIZE, drop_remainder=False).prefetch(prefetch_policy) callbacks.append(BatchLoggerCallback(BATCH_SIZE, train_sample_count, epochs=EPOCHS, ensure_determinism=ENSURE_DETERMINISM)) model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE), loss='categorical_crossentropy', metrics=['accuracy']) model.fit(train_dataset, validation_data=validation_dataset, epochs=EPOCHS, verbose=2, callbacks=callbacks, class_weight=ei_tensorflow.training.get_class_weights(Y_train)) print('') print('Initial training done.', flush=True) # How many epochs we will fine tune the model FINE_TUNE_EPOCHS = 10 # What percentage of the base model's layers we will fine tune FINE_TUNE_PERCENTAGE = 65 print('Fine-tuning best model for {} epochs...'.format(FINE_TUNE_EPOCHS), flush=True) # Load best model from initial training model = ei_tensorflow.training.load_best_model(BEST_MODEL_PATH) # Determine which layer to begin fine tuning at model_layer_count = len(model.layers) fine_tune_from = math.ceil(model_layer_count * ((100 - FINE_TUNE_PERCENTAGE) / 100)) # Allow the entire base model to be trained model.trainable = True # Freeze all the layers before the 'fine_tune_from' layer for layer in model.layers[:fine_tune_from]: layer.trainable = False model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.000045), loss='categorical_crossentropy', metrics=['accuracy']) model.fit(train_dataset, epochs=FINE_TUNE_EPOCHS, verbose=2, validation_data=validation_dataset, callbacks=callbacks, class_weight=ei_tensorflow.training.get_class_weights(Y_train) )
Input layer (27,648 features)
MobileNetV2 96x96 0.35 (final layer: 16 neurons, 0.1 dropout)
Output layer (2 classes)


Model version: