Jenny Plunkett / Forest Fires 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 number for the train/validate split (between 0 and 1)

Neural network architecture

import math 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 from tensorflow.keras.optimizers import Adam, Adadelta from tensorflow.keras.losses import categorical_crossentropy sys.path.append('./resources/libraries') import WEIGHTS_PATH = './transfer-learning-weights/keras/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_0.35_96.h5' # Download the model weights root_url = '' 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')) BATCH_SIZE = 32 EPOCHS = args.epochs or 20 LEARNING_RATE = args.learning_rate or 0.0005 train_dataset = train_dataset.batch(BATCH_SIZE, drop_remainder=False) validation_dataset = validation_dataset.batch(BATCH_SIZE, drop_remainder=False) callbacks.append(BatchLoggerCallback(BATCH_SIZE, train_sample_count, epochs=EPOCHS)) model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE), loss='categorical_crossentropy', metrics=['accuracy']), validation_data=validation_dataset, epochs=EPOCHS, verbose=2, callbacks=callbacks) 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 = # 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']), epochs=FINE_TUNE_EPOCHS, verbose=2, validation_data=validation_dataset, callbacks=callbacks, class_weight=None )
Input layer (27,648 features)
Output layer (2 classes)