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)
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, AveragePooling2D, BatchNormalization, TimeDistributed, Permute, ReLU, Softmax
from tensorflow.keras.optimizers import Adam
EPOCHS = args.epochs or 75
LEARNING_RATE = args.learning_rate or 0.0005
# 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)
# model architecture
model = Sequential()
model.add(Dense(20, activation='relu',
activity_regularizer=tf.keras.regularizers.l1(0.00001)))
model.add(Dense(10, activation='relu',
activity_regularizer=tf.keras.regularizers.l1(0.00001)))
model.add(Dense(classes, name='y_pred', activation='softmax'))
# 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.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 (3 features)
Dense layer (20 neurons)
Dense layer (10 neurons)
Output layer (2 classes)
Model
Model version: