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, DepthwiseConv1D, Reshape
from tensorflow.keras.optimizers import Adam
EPOCHS = 500
# 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()
# # Middle layers
# model.add(Reshape(target_shape=(20, 9),
# input_shape=(180,)))
# model.add(DepthwiseConv1D(3, # kernel size
# strides=1,
# padding='same',
# depth_multiplier=1,
# data_format='channels_last',
# activation='relu'))
# # Flatten and DNN for classification
# model.add(Flatten())
# model.add(Dropout(0.25))
# model.add(Dense(80,
# activation=tf.keras.activations.relu))
# model.add(Dropout(0.25))
model.add(Dense(80, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(40, activation='relu'))
model.add(Dropout(0.25))
# Final model (for regression)
model.add(Dense(classes,
name='y_pred',
activation='linear'))
# this controls the learning rate
opt = Adam(learning_rate=0.005, 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='mean_squared_error', optimizer=opt)
model.fit(train_dataset, epochs=EPOCHS, validation_data=validation_dataset, verbose=2, callbacks=callbacks)
# 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 (180 features)
Dense layer (80 neurons)
Dropout (rate 0.25)
Dense layer (40 neurons)
Dropout (rate 0.25)
Output layer (1 value)
Model
Model version: