Training settings
Please provide a valid training processor option
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(Dense(90, activation='tanh',
activity_regularizer=tf.keras.regularizers.l1(0.00001)))
model.add(Dropout(0.25))
model.add(Dense(120, activation='tanh',
activity_regularizer=tf.keras.regularizers.l1(0.00001)))
model.add(Dropout(0.25))
model.add(Dense(90, activation='tanh',
activity_regularizer=tf.keras.regularizers.l1(0.00001)))
model.add(Dropout(0.25))
model.add(Dense(12, activation='tanh',
activity_regularizer=tf.keras.regularizers.l1(0.00001)))
model.add(Dense(classes, activation='softmax', name='y_pred'))
# this controls the learning rate
opt = Adam(lr=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))
callbacks = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=30)
# train the neural network
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
model.fit(train_dataset, epochs=600, validation_data=validation_dataset, verbose=2, callbacks=callbacks)
Input layer (48 features)
Dense layer (90 neurons)
Dense layer (120 neurons)
Dense layer (90 neurons)
Dense layer (12 neurons)
Output layer (11 classes)
Model
Model version:
