radar_outdoor_moving_object_test - NN Classifier

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Target: Arduino Portenta H7 (Cortex-M7 480MHz)

Neural Network settings

Training settings

Number of training cycles

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Learning rate

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Advanced training settings

Validation set size

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Split train/validation set on metadata key

Batch size

Auto-balance dataset

Profile int8 model

Neural network architecture

import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, InputLayer, Dropout, Conv1D, Conv2D, Flatten, Reshape, MaxPooling1D, MaxPooling2D, BatchNormalization, TimeDistributed from tensorflow.keras.optimizers import Adam # model architecture model = Sequential() model.add(Conv2D(8, kernel_size=5, activation='relu', kernel_constraint=tf.keras.constraints.MaxNorm(1), padding='same')) model.add(MaxPooling2D(pool_size=2, strides=2, padding='same')) model.add(Conv2D(8, kernel_size=5, activation='relu', kernel_constraint=tf.keras.constraints.MaxNorm(1), padding='same')) model.add(MaxPooling2D(pool_size=2, strides=2, padding='same')) model.add(Dropout(0.5)) model.add(Flatten()) model.add(Dense(8, activation='relu', activity_regularizer=tf.keras.regularizers.l1(0.00001))) model.add(Dense(classes, activation='softmax', name='y_pred')) # this controls the learning rate opt = Adam(learning_rate=0.0005, beta_1=0.9, beta_2=0.999) # this controls the batch size, or you can manipulate the tf.data.Dataset objects yourself BATCH_SIZE = 32 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)) # train the neural network model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) model.fit(train_dataset, epochs=100, validation_data=validation_dataset, verbose=2, callbacks=callbacks, class_weight=ei_tensorflow.training.get_class_weights(Y_train)) # Use this flag to disable per-channel quantization for a model. # This can reduce RAM usage for convolutional models, but may have # an impact on accuracy. disable_per_channel_quantization = False

Input layer (76,800 features)

2D conv / pool layer (8 filters, 5 kernel size, 2 layers)

Dropout (rate 0.5)

Flatten layer

Dense layer (8 neurons)

Output layer (4 classes)

Model

Model version:

Did you know? You have access to the full set of Keras layers through the Expert view (click on to switch),
or can even bring your own model (in PyTorch, Keras or scikit-learn).

Layer type
Dense Fully connected layer, the simplest form of a neural network layer. Use this for processed data, such as the output of a spectral analysis DSP block.
1D Convolution / pooling Learn features that take spatial information into account along a single dimension. Use this for raw data, or for DSP blocks that output spatial data, such as the MFCC block.
2D Convolution / pooling Learn features that take spatial information into account along two dimensions. Use this for raw data, or for DSP blocks that output spatial data, such as the MFCC block.
Reshape Turn one-dimensional data from a DSP block into multi-dimensional data. Use this as an input to a convolutional layer. Use this for deep learning on raw data, or to process MFCC output.
Flatten Flatten multi-dimensional data into a single dimension. You need to flatten data from a convolutional layer before returning.
Dropout Reduce the risk of a model overfitting your dataset by randomly cutting a fraction of network connections during training. Can be helpful if your model's training performance is better than its validation performance.

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	Name
	DS David Schwarz Edge Impulse staff

Neural Network settings

Training settings

Advanced training settings

Neural network architecture

Model

Dense

1D Convolution / pooling

2D Convolution / pooling

Reshape

Flatten

Dropout

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Last training performance (validation set)

Accuracy

Loss

Confusion matrix (validation set)

Feature explorer (full training set)

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Neural Network settings

Training settings

Advanced training settings

Neural network architecture

Model

Description

Last training performance (validation set)

Accuracy

Loss

Confusion matrix (validation set)

Feature explorer (full training set)

Settings