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 confidence rating (numeric only)

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 from tensorflow.keras.optimizers import Adam sys.path.append('./resources/libraries') import ei_tensorflow.training # model architecture model = Sequential() model.add(Reshape((int(input_length / 1), 1), input_shape=(input_length, ))) model.add(Conv1D(8, kernel_size=4, activation='relu', padding='same')) model.add(MaxPooling1D(pool_size=2, strides=2, padding='same')) model.add(Conv1D(16, kernel_size=2, activation='relu', padding='same')) model.add(MaxPooling1D(pool_size=2, strides=2, padding='same')) model.add(Flatten()) model.add(Dense(classes, activation='softmax', name='y_pred')) # this controls the learning rate opt = Adam(lr=0.0001, 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, validation_dataset = ei_tensorflow.training.set_batch_size(BATCH_SIZE, train_dataset, validation_dataset) 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=500, validation_data=validation_dataset, verbose=2, callbacks=callbacks)
Input layer (40 features)
Output layer (3 features)