David Schwarz / Ball Bearing Fault Detection 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.models import Sequential from tensorflow.keras.layers import Dense, InputLayer, ReLU, Softmax, Dropout, Conv1D, Conv2D, Flatten, Reshape, MaxPooling1D, MaxPooling2D, BatchNormalization, TimeDistributed from tensorflow.keras.optimizers import Adam # model architecture model = Sequential() model.add(Dense(64, activity_regularizer=tf.keras.regularizers.l1(0.00001))) model.add(ReLU()) model.add(Dropout(0.5)) model.add(Dense(32, activity_regularizer=tf.keras.regularizers.l1(0.00001))) model.add(ReLU()) model.add(Dense(classes, name='y_pred')) model.add(Softmax()) # this controls the learning rate opt = Adam(learning_rate=0.00005, beta_1=0.9, beta_2=0.999) EPOCHS = 100 # 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, epochs=EPOCHS)) # train the neural network model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) model.fit(train_dataset, epochs=EPOCHS, 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 (1,000 features)
Dense layer (64 neurons)
Dropout (rate 0.5)
Dense layer (32 neurons)
Output layer (2 classes)