c4a1737fa7
In der config Datei kann man nun einstellen, ob während des Training mit leaky relu, bei einer Performance von 100% auf relu geswitched wird (d.h. leaky relu mit slope = 0.0). In der cnn_trainin.py musste ich beim Lesen und Laden der config.json aufgrund eines komischen Errors beim Ausführen der .sh-file was ändern.
424 lines
15 KiB
Python
424 lines
15 KiB
Python
import torch
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import numpy as np
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import datetime
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import argh
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import time
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import os
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import json
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from jsmin import jsmin
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from functions.alicorn_data_loader import alicorn_data_loader
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from functions.train import train
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from functions.test import test
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from functions.make_cnn import make_cnn
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from functions.set_seed import set_seed
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from functions.plot_intermediate import plot_intermediate
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from functions.create_logger import create_logger
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# to disable logging output from Tensorflow
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
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from torch.utils.tensorboard import SummaryWriter
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def main(
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idx_conv_out_channels_list: int = 0,
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idx_conv_kernel_sizes: int = 0,
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idx_conv_stride_sizes: int = 0,
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seed_counter: int = 0,
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) -> None:
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config_filenname = "config.json"
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with open(config_filenname, "r") as file_handle:
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file_contents = file_handle.read()
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f_contents = jsmin(file_contents)
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config = json.loads(f_contents)
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# config = json.loads(jsmin(file_handle.read()))
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# get model information:
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output_channels = config["conv_out_channels_list"][idx_conv_out_channels_list]
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logger = create_logger(
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save_logging_messages=bool(config["save_logging_messages"]),
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display_logging_messages=bool(config["display_logging_messages"]),
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model_name=str(output_channels),
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)
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# network settings:
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conv_out_channels_list: list[list[int]] = config["conv_out_channels_list"]
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conv_kernel_sizes: list[list[int]] = config["conv_kernel_sizes"]
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conv_stride_sizes: list[int] = config["conv_stride_sizes"]
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num_pfinkel: list = np.arange(
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int(config["num_pfinkel_start"]),
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int(config["num_pfinkel_stop"]),
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int(config["num_pfinkel_step"]),
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).tolist()
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run_network(
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out_channels=conv_out_channels_list[int(idx_conv_out_channels_list)],
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kernel_size=conv_kernel_sizes[int(idx_conv_kernel_sizes)],
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stride=conv_stride_sizes[int(idx_conv_stride_sizes)],
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activation_function=str(config["activation_function"]),
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train_first_layer=bool(config["train_first_layer"]),
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seed_counter=seed_counter,
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minimum_learning_rate=float(config["minimum_learning_rate"]),
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conv_0_kernel_size=int(config["conv_0_kernel_size"]),
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mp_1_kernel_size=int(config["mp_1_kernel_size"]),
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mp_1_stride=int(config["mp_1_stride"]),
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batch_size_train=int(config["batch_size_train"]),
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batch_size_test=int(config["batch_size_test"]),
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learning_rate=float(config["learning_rate"]),
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max_epochs=int(config["max_epochs"]),
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save_model=bool(config["save_model"]),
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stimuli_per_pfinkel=int(config["stimuli_per_pfinkel"]),
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num_pfinkel=num_pfinkel,
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logger=logger,
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save_ever_x_epochs=int(config["save_ever_x_epochs"]),
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scheduler_patience=int(config["scheduler_patience"]),
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condition=str(config["condition"]),
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data_path=str(config["data_path"]),
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pooling_type=str(config["pooling_type"]),
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conv_0_enable_softmax=bool(config["conv_0_enable_softmax"]),
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scale_data=int(config["scale_data"]),
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use_scheduler=bool(config["use_scheduler"]),
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use_adam=bool(config["use_adam"]),
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use_plot_intermediate=bool(config["use_plot_intermediate"]),
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leak_relu_negative_slope=float(config["leak_relu_negative_slope"]),
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switch_leakyR_to_relu=bool(config["switch_leakyR_to_relu"]),
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scheduler_verbose=bool(config["scheduler_verbose"]),
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scheduler_factor=float(config["scheduler_factor"]),
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precision_100_percent=int(config["precision_100_percent"]),
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scheduler_threshold=float(config["scheduler_threshold"]),
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)
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def run_network(
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out_channels: list[int],
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kernel_size: list[int],
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num_pfinkel: list,
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logger,
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stride: int,
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activation_function: str,
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train_first_layer: bool,
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seed_counter: int,
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minimum_learning_rate: float,
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conv_0_kernel_size: int,
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mp_1_kernel_size: int,
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mp_1_stride: int,
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scheduler_patience: int,
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batch_size_train: int,
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batch_size_test: int,
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learning_rate: float,
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max_epochs: int,
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save_model: bool,
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stimuli_per_pfinkel: int,
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save_ever_x_epochs: int,
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condition: str,
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data_path: str,
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pooling_type: str,
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conv_0_enable_softmax: bool,
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scale_data: float,
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use_scheduler: bool,
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use_adam: bool,
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use_plot_intermediate: bool,
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leak_relu_negative_slope: float,
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switch_leakyR_to_relu: bool,
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scheduler_verbose: bool,
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scheduler_factor: float,
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precision_100_percent: int,
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scheduler_threshold: float,
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) -> None:
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# define device:
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device_str: str = "cuda:0" if torch.cuda.is_available() else "cpu"
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logger.info(f"Using {device_str} device")
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device: torch.device = torch.device(device_str)
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torch.set_default_dtype(torch.float32)
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# switch to relu if using leaky relu
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switched_to_relu: bool = False
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# -------------------------------------------------------------------
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logger.info("-==- START -==-")
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train_accuracy: list[float] = []
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train_losses: list[float] = []
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train_loss: list[float] = []
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test_accuracy: list[float] = []
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test_losses: list[float] = []
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# prepare data:
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logger.info(num_pfinkel)
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logger.info(condition)
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logger.info("Loading training data")
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data_train = alicorn_data_loader(
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num_pfinkel=num_pfinkel,
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load_stimuli_per_pfinkel=stimuli_per_pfinkel,
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condition=condition,
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logger=logger,
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data_path=data_path,
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)
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logger.info("Loading test data")
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data_test = alicorn_data_loader(
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num_pfinkel=num_pfinkel,
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load_stimuli_per_pfinkel=stimuli_per_pfinkel,
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condition=condition,
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logger=logger,
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data_path=data_path,
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)
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logger.info("Loading done!")
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# data loader
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loader_train = torch.utils.data.DataLoader(
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data_train, shuffle=True, batch_size=batch_size_train
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)
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loader_test = torch.utils.data.DataLoader(
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data_test, shuffle=False, batch_size=batch_size_test
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)
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previous_test_acc: float = -1
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# set seed for reproducibility
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set_seed(seed=int(seed_counter), logger=logger)
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# number conv layer:
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if train_first_layer:
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num_conv_layers = len(out_channels)
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else:
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num_conv_layers = len(out_channels) if len(out_channels) >= 2 else 1
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# determine num conv layers
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model_name = (
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f"ArghCNN_numConvLayers{num_conv_layers}"
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f"_outChannels{out_channels}_kernelSize{kernel_size}_"
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f"{activation_function}_stride{stride}_"
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f"trainFirstConvLayer{train_first_layer}_"
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f"seed{seed_counter}_{condition}"
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)
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current = datetime.datetime.now().strftime("%d%m-%H%M")
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# new tb session
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os.makedirs("tb_runs", exist_ok=True)
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path: str = os.path.join("tb_runs", f"{model_name}")
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tb = SummaryWriter(path)
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# --------------------------------------------------------------------------
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# print network configuration:
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logger.info("----------------------------------------------------")
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logger.info(f"Number conv layers: {num_conv_layers}")
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logger.info(f"Output channels: {out_channels}")
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logger.info(f"Kernel sizes: {kernel_size}")
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logger.info(f"Stride: {stride}")
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logger.info(f"Activation function: {activation_function}")
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logger.info(f"Training conv 0: {train_first_layer}")
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logger.info(f"Seed: {seed_counter}")
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logger.info(f"LR-scheduler patience: {scheduler_patience}")
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logger.info(f"Pooling layer kernel: {mp_1_kernel_size}, stride: {mp_1_stride}")
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# define model:
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model = make_cnn(
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conv_out_channels_list=out_channels,
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conv_kernel_size=kernel_size,
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conv_stride_size=stride,
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conv_activation_function=activation_function,
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train_conv_0=train_first_layer,
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conv_0_kernel_size=conv_0_kernel_size,
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mp_1_kernel_size=mp_1_kernel_size,
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mp_1_stride=mp_1_stride,
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logger=logger,
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pooling_type=pooling_type,
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conv_0_enable_softmax=conv_0_enable_softmax,
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l_relu_negative_slope=leak_relu_negative_slope,
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).to(device)
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logger.info(model)
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old_params: dict = {}
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for name, param in model.named_parameters():
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old_params[name] = param.data.detach().cpu().clone()
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# pararmeters for training:
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param_list: list = []
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for i in range(0, len(model)):
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if (not train_first_layer) and (i == 0):
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pass
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else:
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for name, param in model[i].named_parameters():
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logger.info(f"Learning parameter: layer: {i} name: {name}")
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param_list.append(param)
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for name, param in model.named_parameters():
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assert (
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torch.isfinite(param.data).sum().cpu()
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== torch.tensor(param.data.size()).prod()
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), name
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# optimizer and learning rate scheduler
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if use_adam:
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optimizer = torch.optim.Adam(param_list, lr=learning_rate)
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else:
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optimizer = torch.optim.SGD(param_list, lr=learning_rate) # type: ignore
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if use_scheduler:
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scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
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optimizer,
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patience=scheduler_patience,
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eps=minimum_learning_rate / 10,
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verbose=scheduler_verbose,
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factor=scheduler_factor,
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threshold=scheduler_threshold,
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)
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# training loop:
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logger.info("-==- Data and network loader: Done -==-")
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t_dis0 = time.perf_counter()
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for epoch in range(1, max_epochs + 1):
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# train
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logger.info("-==- Training... -==-")
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running_loss = train(
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model=model,
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loader=loader_train,
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optimizer=optimizer,
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epoch=epoch,
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device=device,
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tb=tb,
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test_acc=previous_test_acc,
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logger=logger,
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train_accuracy=train_accuracy,
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train_losses=train_losses,
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train_loss=train_loss,
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scale_data=scale_data,
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)
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# logging:
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logger.info("")
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logger.info("Check for changes in the weights:")
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for name, param in model.named_parameters():
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if isinstance(old_params[name], torch.Tensor) and isinstance(
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param.data, torch.Tensor
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):
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temp_torch = param.data.detach().cpu().clone()
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if old_params[name].ndim == temp_torch.ndim:
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if old_params[name].size() == temp_torch.size():
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abs_diff = torch.abs(old_params[name] - temp_torch).max()
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logger.info(f"Parameter {name}: {abs_diff:.3e}")
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old_params[name] = temp_torch
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logger.info("")
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logger.info("-==- Testing... -==-")
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previous_test_acc = test( # type: ignore
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model=model,
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loader=loader_test,
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device=device,
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tb=tb,
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epoch=epoch,
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logger=logger,
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test_accuracy=test_accuracy,
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test_losses=test_losses,
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scale_data=scale_data,
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)
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logger.info(f"Time required: {time.perf_counter()-t_dis0:.2e} sec")
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# save model after every 100th epoch:
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if save_model and (epoch % save_ever_x_epochs == 0):
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pt_filename: str = f"{model_name}_{epoch}Epoch_{current}.pt"
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logger.info("")
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logger.info(f"Saved model: {pt_filename}")
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os.makedirs("trained_models", exist_ok=True)
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torch.save(
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model,
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os.path.join(
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"trained_models",
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pt_filename,
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),
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)
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# check nan
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for name, param in model.named_parameters():
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assert (
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torch.isfinite(param.data).sum().cpu()
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== torch.tensor(param.data.size()).prod()
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), name
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# update scheduler
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if use_scheduler:
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if scheduler_verbose and isinstance(scheduler.best, float):
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logger.info(
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"Step LR scheduler: "
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f"Loss: {running_loss:.2e} "
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f"Best: {scheduler.best:.2e} "
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f"Delta: {running_loss-scheduler.best:.2e} "
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f"Threshold: {scheduler.threshold:.2e} "
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f"Number of bad epochs: {scheduler.num_bad_epochs} "
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f"Patience: {scheduler.patience} "
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)
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scheduler.step(running_loss)
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# stop learning: lr too small
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if optimizer.param_groups[0]["lr"] <= minimum_learning_rate:
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logger.info("Learning rate is too small. Stop training.")
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break
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# stop learning: done
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if round(previous_test_acc, precision_100_percent) == 100.0:
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if activation_function == "leaky relu":
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if switch_leakyR_to_relu and not switched_to_relu:
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logger.info(
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"100% test performance reached. Switching to LeakyReLU slope 0.0."
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)
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for name, module in model.named_children():
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if isinstance(module, torch.nn.LeakyReLU):
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module.negative_slope = 0.0
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logger.info(model)
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switched_to_relu = True
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else:
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logger.info("100% test performance reached. Stop training.")
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break
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if use_plot_intermediate:
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plot_intermediate(
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train_accuracy=train_accuracy,
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test_accuracy=test_accuracy,
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train_losses=train_losses,
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test_losses=test_losses,
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save_name=model_name,
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)
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os.makedirs("performance_data", exist_ok=True)
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np.savez(
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os.path.join("performance_data", f"performances_{model_name}.npz"),
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output_channels=np.array(out_channels),
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train_accuracy=np.array(train_accuracy),
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test_accuracy=np.array(test_accuracy),
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train_losses=np.array(train_losses),
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test_losses=np.array(test_losses),
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)
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# end TB session:
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tb.close()
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# print model name:
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logger.info("")
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logger.info(f"Saved model: {model_name}_{epoch}Epoch_{current}")
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if save_model:
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os.makedirs("trained_models", exist_ok=True)
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torch.save(
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model,
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os.path.join(
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"trained_models",
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f"{model_name}_{epoch}Epoch_{current}.pt",
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),
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)
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if __name__ == "__main__":
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argh.dispatch_command(main)
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