Edge Impulse Experts / Fall_Detection_using_Transformer Public

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 tensorflow as tf from tensorflow.keras import Model from tensorflow.keras.layers import Dense, Input, MultiHeadAttention, Reshape, Dropout, GlobalAveragePooling1D, Conv1D, LayerNormalization, Normalization from tensorflow.keras.optimizers import Adam EPOCHS = 10 LEARNING_RATE = 0.0005 BATCH_SIZE = 32 # model architecture def transformer_encoder(inputs, head_size, num_heads, ff_dim, dropout=0): # Normalization and Attention x = LayerNormalization(epsilon=1e-6)(inputs) x = MultiHeadAttention( key_dim=head_size, num_heads=num_heads, dropout=dropout )(x, x) x = Dropout(dropout)(x) res = x + inputs # Feed Forward Part x = LayerNormalization(epsilon=1e-6)(res) x = Conv1D(filters=ff_dim, kernel_size=1, activation="relu")(x) x = Dropout(dropout)(x) x = Conv1D(filters=inputs.shape[-1], kernel_size=1)(x) return x + res def build_model( input_shape, head_size, num_heads, ff_dim, num_transformer_blocks, mlp_units, dropout=0, mlp_dropout=0, ): inputs = Input(shape=input_shape) x = Reshape([int(input_length/3), 3])(inputs) # pre-calculated mean and variance x = Normalization(axis=-1, mean=[-0.047443, -6.846333, -1.057524], variance=[16.179484, 33.019396, 22.892909])(x) for _ in range(num_transformer_blocks): x = transformer_encoder(x, head_size, num_heads, ff_dim, dropout) x = GlobalAveragePooling1D(data_format="channels_first")(x) for dim in mlp_units: x = Dense(dim, activation="relu")(x) x = Dropout(mlp_dropout)(x) outputs = Dense(classes, activation="softmax")(x) return Model(inputs, outputs) input_shape = (input_length, ) model = build_model( input_shape, head_size=64, num_heads=2, ff_dim=4, num_transformer_blocks=1, mlp_units=[32], mlp_dropout=0.40, dropout=0.25, ) # this controls the learning rate opt = Adam(learning_rate=LEARNING_RATE, beta_1=0.9, beta_2=0.999) callbacks.append(BatchLoggerCallback(BATCH_SIZE, train_sample_count, epochs=EPOCHS)) # train the neural network model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) model.summary() train_dataset = train_dataset.batch(BATCH_SIZE, drop_remainder=False) validation_dataset = validation_dataset.batch(BATCH_SIZE, drop_remainder=False) model.fit(train_dataset, epochs=EPOCHS, validation_data=validation_dataset, verbose=2, callbacks=callbacks) disable_per_channel_quantization = False
Input layer (600 features)
Dense layer (20 neurons)
Dense layer (10 neurons)
Output layer (2 classes)


Model version: