V.validate_percentage_string.value = "40"
V.mini_batch_string.value = "32"
V.testing_files = ""
V.replicates_string.value = "1"
for batch_seed in ["1", "-1"]:
V.batch_seed_string.value = batch_seed
for weights_seed in ["1", "-1"]:
V.weights_seed_string.value = weights_seed
V.logs_folder.value = os.path.join(repo_path,
"test/scratch/seeds/trained-classifier-bs="+batch_seed+"-ws="+weights_seed)
asyncio.run(C.train_actuate())
wait_for_job(M.status_ticker_queue)
check_file_exists(os.path.join(V.logs_folder.value, "train1.log"))
check_file_exists(os.path.join(V.logs_folder.value, "train_1r.log"))
check_file_exists(os.path.join(V.logs_folder.value,
"train_1r", "ckpt-"+V.nsteps_string.value+".index"))
run(["hetero", "stop"], stdout=PIPE, stderr=STDOUT)
import tensorflow as tf
import numpy as np
same_weights = ["trained-classifier-bs=1-ws=1/train_1r/ckpt-0",
"trained-classifier-bs=-1-ws=1/train_1r/ckpt-0"]
diff_weights = ["trained-classifier-bs=1-ws=-1/train_1r/ckpt-0",
"trained-classifier-bs=-1-ws=-1/train_1r/ckpt-0"]
wavpath_noext = os.path.join(
repo_path,
"test/scratch/tutorial-py/groundtruth-data/round1/PS_20130625111709_ch3")
V.wavtfcsvfiles_string.value = wavpath_noext + ".wav"
V.time_sigma_string.value = "6,3"
V.time_smooth_ms_string.value = "6.4"
V.frequency_n_ms_string.value = "25.6"
V.frequency_nw_string.value = "4"
V.frequency_p_string.value = "0.1,1.0"
V.frequency_smooth_ms_string.value = "25.6"
asyncio.run(C.detect_actuate())
wait_for_job(M.status_ticker_queue)
check_file_exists(wavpath_noext + "-detect.log")
check_file_exists(wavpath_noext + "-detected.csv")
count_lines_with_word(wavpath_noext + "-detected.csv", "time", 543)
count_lines_with_word(wavpath_noext + "-detected.csv", "frequency", 45)
count_lines_with_word(wavpath_noext + "-detected.csv", "ambient", 1138)
V.context_ms_string.value = "204.8"
V.shiftby_ms_string.value = "0.0"
V.representation.value = "mel-cepstrum"
V.window_ms_string.value = "6.4"
V.mel_dct_string.value = "7,7"
V.stride_ms_string.value = "1.6"
V.dropout_string.value = "0.5"
V.optimizer.value = "adam"
V.learning_rate_string.value = "0.0002"
V.kernel_sizes_string.value = "5,3,3"
V.weights_seed_string.value = "1"
V.replicates_string.value = "1"
for representation in ["waveform", "spectrogram", "mel-cepstrum"]:
V.representation.value = representation
for stride_after_layer in ["0", "65535"]:
V.model_parameters["stride_after_layer"].value = stride_after_layer
V.logs_folder.value = os.path.join(
repo_path, "test/scratch/freeze-classify",
"trained-classifier-r=" + representation + "-s=" +
stride_after_layer)
asyncio.run(C.train_actuate())
wait_for_job(M.status_ticker_queue)
check_file_exists(os.path.join(V.logs_folder.value, "train1.log"))
check_file_exists(os.path.join(V.logs_folder.value, "train_1r.log"))
check_file_exists(
os.path.join(V.logs_folder.value, "train_1r",
"ckpt-" + V.nsteps_string.value + ".index"))
V.model_file.value = os.path.join(
V.logs_folder.value, "train_" + V.replicates_string.value + "r",
"ckpt-" + V.nsteps_string.value + ".meta")
V.wavtfcsvfiles_string.value = os.path.join(
repo_path,
"test/scratch/freeze-classify/groundtruth-data/round1/PS_20130625111709_ch3.wav"
)
V.prevalences_string.value = ""
for nwindows in ["1", "9"]:
V.init(None)
C.init(None)
run(["hetero", "start", "1", "1", "1"])
V.groundtruth_folder.value = os.path.join(
repo_path, "test/scratch/congruence/groundtruth-data")
V.testfiles_string.value = ""
V.validationfiles_string.value = "recording1.wav,recording2.wav,recording3.wav,recording4.wav"
asyncio.run(C.congruence_actuate())
wait_for_job(M.status_ticker_queue)
wavpath_noext = V.validationfiles_string.value[:-4]
check_file_exists(os.path.join(V.groundtruth_folder.value, "congruence.log"))
for i in range(1, 8):
check_file_exists(
os.path.join(V.groundtruth_folder.value,
"congruence.tic.label" + str(i) + ".csv"))
check_file_exists(
os.path.join(V.groundtruth_folder.value,
"congruence.word.label" + str(i) + ".csv"))
l1 = pd.read_csv(
os.path.join(V.groundtruth_folder.value, "congruence.word.label1.csv"))
pr1 = l1.loc[l1['Unnamed: 0'] == '1.0pr']
check_value(pr1, "everyone", 1)
check_value(pr1, "only person1", 2)
check_value(pr1, "only person2", 3)