def evaluate_model_area(model_name: str, test_words_type: str) -> None:
r"""MAKEDOC: what is evaluate_model_area doing?"""
logg = logging.getLogger(f"c.{__name__}.evaluate_model_area")
# logg.setLevel("INFO")
logg.debug("Start evaluate_model_area")
# magic to fix the GPUs
setup_gpus()
# # VAN_opa1_lr05_bs32_en15_dsaug07_wLTall
# hypa = {
# "batch_size_type": "32",
# "dataset_name": "aug07",
# "epoch_num_type": "15",
# "learning_rate_type": "03",
# "net_type": "VAN",
# "optimizer_type": "a1",
# # "words_type": "LTall",
# "words_type": train_words_type,
# }
# # use_validation = True
# use_validation = False
# dataset_name = hypa["dataset_name"]
# get the model name
# model_name = build_area_name(hypa, use_validation)
logg.debug(f"model_name: {model_name}")
dataset_re = re.compile("_ds(.*?)_")
match = dataset_re.search(model_name)
if match is not None:
logg.debug(f"match[1]: {match[1]}")
dataset_name = match[1]
train_words_type_re = re.compile("_w(.*?)[_.]")
match = train_words_type_re.search(model_name)
if match is not None:
logg.debug(f"match[1]: {match[1]}")
train_words_type = match[1]
# load the model
model_folder = Path("trained_models") / "area"
model_path = model_folder / f"{model_name}.h5"
model = tf_models.load_model(model_path)
# model.summary()
train_words = words_types[train_words_type]
logg.debug(f"train_words: {train_words}")
test_words = words_types[test_words_type]
logg.debug(f"test_words: {test_words}")
# input data
processed_path = Path("data_proc") / f"{dataset_name}"
data, labels = load_processed(processed_path, test_words)
logg.debug(f"list(data.keys()): {list(data.keys())}")
logg.debug(f"data['testing'].shape: {data['testing'].shape}")
# evaluate on the words you trained on
logg.debug("Evaluate on test data:")
model.evaluate(data["testing"], labels["testing"])
# model.evaluate(data["validation"], labels["validation"])
# predict labels/cm/fscore
y_pred = model.predict(data["testing"])
cm = pred_hot_2_cm(labels["testing"], y_pred, test_words)
# y_pred = model.predict(data["validation"])
# cm = pred_hot_2_cm(labels["validation"], y_pred, test_words)
fscore = analyze_confusion(cm, test_words)
logg.debug(f"fscore: {fscore}")
fig, ax = plt.subplots(figsize=(12, 12))
plot_confusion_matrix(cm, ax, model_name, test_words, fscore, train_words)
fig_name = f"{model_name}_test{test_words_type}_cm.{{}}"
cm_folder = Path("plot_results") / "cm"
if not cm_folder.exists():
cm_folder.mkdir(parents=True, exist_ok=True)
plot_cm_path = cm_folder / fig_name.format("png")
fig.savefig(plot_cm_path)
plot_cm_path = cm_folder / fig_name.format("pdf")
fig.savefig(plot_cm_path)
plt.show()
def train_transfer(
hypa: ty.Dict[str, str],
force_retrain: bool,
use_validation: bool,
trained_folder: Path,
root_info_folder: Path,
tensorboard_logs_folder: Path,
) -> None:
"""MAKEDOC: what is train_transfer doing?
https://www.tensorflow.org/guide/keras/transfer_learning/#build_a_model
"""
logg = logging.getLogger(f"c.{__name__}.train_transfer")
# logg.setLevel("INFO")
logg.debug("Start train_transfer")
##########################################################
# Setup folders
##########################################################
# name the model
model_name = build_transfer_name(hypa, use_validation)
logg.debug(f"model_name: {model_name}")
# save the trained model here
model_path = trained_folder / f"{model_name}.h5"
placeholder_path = trained_folder / f"{model_name}.txt"
# check if this model has already been trained
if placeholder_path.exists():
if force_retrain:
logg.warn("\nRETRAINING MODEL!!\n")
else:
logg.debug("Already trained")
return
# save info regarding the model training in this folder
model_info_folder = root_info_folder / model_name
if not model_info_folder.exists():
model_info_folder.mkdir(parents=True, exist_ok=True)
# magic to fix the GPUs
setup_gpus()
##########################################################
# Load data
##########################################################
# grab a few hypas
words_type = hypa["words_type"]
datasets_type = hypa["datasets_type"]
# get the partition of the data
partition, ids2labels = prepare_partitions(words_type)
# get the word list
words = words_types[words_type]
num_labels = len(words)
# get the dataset name list
datasets_types, datasets_shapes = get_datasets_types()
dataset_names = datasets_types[datasets_type]
dataset_shape = datasets_shapes[datasets_type]
# the shape of each sample
input_shape = (*dataset_shape, 3)
# from hypa extract training param (epochs, batch, opt, ...)
training_param = get_training_param_transfer(hypa, use_validation,
tensorboard_logs_folder,
model_path)
# load datasets
processed_folder = Path("data_split")
data_split_paths = [processed_folder / f"{dn}" for dn in dataset_names]
# data, labels = load_triple(data_paths, words)
# assemble the gen_param for the generators
gen_param = {
"dim": dataset_shape,
"batch_size": training_param["batch_sizes"][0],
"shuffle": True,
"label_names": words,
"data_split_paths": data_split_paths,
}
# maybe concatenate the valdation and training lists
val_generator: ty.Optional[AudioGenerator] = None
if use_validation:
val_generator = AudioGenerator(partition["validation"], ids2labels,
**gen_param)
logg.debug("Using validation data")
else:
partition["training"].extend(partition["validation"])
logg.debug("NOT using validation data")
# create the training generator with the modified (maybe) list of IDs
training_generator = AudioGenerator(partition["training"], ids2labels,
**gen_param)
logg.debug(f"len(training_generator): {len(training_generator)}")
###### always create the test generator
# do not shuffle the test data
gen_param["shuffle"] = False
# do not batch it, no loss of stray data at the end
gen_param["batch_size"] = 1
testing_generator = AudioGenerator(partition["testing"], ids2labels,
**gen_param)
##########################################################
# Setup model
##########################################################
# from hypa extract model param
model_param = get_model_param_transfer(hypa, num_labels, input_shape)
# get mean and var to normalize the data
data_mean, data_variance = get_generator_mean_var_cached(
training_generator, words_type, datasets_type, processed_folder)
# get the model
model, base_model = TRAmodel(data_mean=data_mean,
data_variance=data_variance,
**model_param)
model.summary()
# a dict to recreate this training
recap: ty.Dict[str, ty.Any] = {}
recap["words"] = words
recap["hypa"] = hypa
recap["model_param"] = model_param
recap["use_validation"] = use_validation
recap["model_name"] = model_name
recap["batch_sizes"] = training_param["batch_sizes"]
recap["epoch_num"] = training_param["epoch_num"]
recap["version"] = "003"
# logg.debug(f"recap: {recap}")
recap_path = model_info_folder / "recap.json"
recap_path.write_text(json.dumps(recap, indent=4))
##########################################################
# Compile and fit model the first time
##########################################################
model.compile(
optimizer=training_param["opt"][0],
loss=tf_losses.CategoricalCrossentropy(),
metrics=training_param["metrics"][0],
)
results_freeze = model.fit(
training_generator,
validation_data=val_generator,
epochs=training_param["epoch_num"][0],
callbacks=training_param["callbacks"][0],
)
# reload the best weights saved by the ModelCheckpoint
model.load_weights(str(model_path))
##########################################################
# Save results, history, performance
##########################################################
# results_freeze_recap
results_freeze_recap: ty.Dict[str, ty.Any] = {}
results_freeze_recap["model_name"] = model_name
results_freeze_recap["results_recap_version"] = "001"
# save the histories
results_freeze_recap["history_train"] = {
mn: results_freeze.history[mn]
for mn in model.metrics_names
}
if use_validation:
results_freeze_recap["history_val"] = {
f"val_{mn}": results_freeze.history[f"val_{mn}"]
for mn in model.metrics_names
}
# save the results
res_recap_path = model_info_folder / "results_freeze_recap.json"
res_recap_path.write_text(json.dumps(results_freeze_recap, indent=4))
##########################################################
# Compile and fit model the second time
##########################################################
# Unfreeze the base_model. Note that it keeps running in inference mode
# since we passed `training=False` when calling it. This means that
# the batchnorm layers will not update their batch statistics.
# This prevents the batchnorm layers from undoing all the training
# we've done so far.
base_model.trainable = True
model.summary()
model.compile(
optimizer=training_param["opt"][1], # Low learning rate
loss=tf_losses.CategoricalCrossentropy(),
metrics=training_param["metrics"][1],
)
results_full = model.fit(
training_generator,
validation_data=val_generator,
epochs=training_param["epoch_num"][1],
callbacks=training_param["callbacks"][1],
)
# reload the best weights saved by the ModelCheckpoint
model.load_weights(str(model_path))
##########################################################
# Save results, history, performance
##########################################################
results_full_recap: ty.Dict[str, ty.Any] = {}
results_full_recap["model_name"] = model_name
results_full_recap["results_recap_version"] = "001"
# evaluate performance
eval_testing = model.evaluate(testing_generator)
for metrics_name, value in zip(model.metrics_names, eval_testing):
logg.debug(f"{metrics_name}: {value}")
results_full_recap[metrics_name] = value
# compute the confusion matrix
y_pred = model.predict(testing_generator)
y_pred_labels = testing_generator.pred2labelnames(y_pred)
y_true = testing_generator.get_true_labels()
# cm = pred_hot_2_cm(y_true, y_pred, words)
cm = confusion_matrix(y_true, y_pred_labels)
results_full_recap["cm"] = cm.tolist()
# compute the fscore
fscore = analyze_confusion(cm, words)
logg.debug(f"fscore: {fscore}")
results_full_recap["fscore"] = fscore
# plot the cm
fig, ax = plt.subplots(figsize=(12, 12))
plot_confusion_matrix(cm, ax, model_name, words, fscore)
plot_cm_path = model_info_folder / "test_confusion_matrix.png"
fig.savefig(plot_cm_path)
plt.close(fig)
# save the histories
results_full_recap["history_train"] = {
mn: results_full.history[mn]
for mn in model.metrics_names
}
if use_validation:
results_full_recap["history_val"] = {
f"val_{mn}": results_full.history[f"val_{mn}"]
for mn in model.metrics_names
}
# save the results
res_recap_path = model_info_folder / "results_full_recap.json"
res_recap_path.write_text(json.dumps(results_full_recap, indent=4))
# save the trained model
model.save(model_path)
# save the placeholder
placeholder_path.write_text(f"Trained. F-score: {fscore}")
def train_model(hypa, force_retrain):
"""MAKEDOC: What is train_model doing?"""
logg = logging.getLogger(f"c.{__name__}.train_model")
# logg.debug("Starting train_model")
# get the words
words = words_types[hypa["words"]]
# name the model
model_name = build_cnn_name(hypa)
logg.debug(f"model_name: {model_name}")
# save the trained model here
model_folder = Path("trained_models") / "cnn"
if not model_folder.exists():
model_folder.mkdir(parents=True, exist_ok=True)
model_path = model_folder / f"{model_name}.h5"
# logg.debug(f"model_path: {model_path}")
placeholder_path = model_folder / f"{model_name}.txt"
# check if this model has already been trained
if placeholder_path.exists():
if force_retrain:
logg.warn("\nRETRAINING MODEL!!\n")
else:
logg.debug("Already trained")
return
# save info regarding the model training in this folder
info_folder = Path("info") / "cnn" / model_name
if not info_folder.exists():
info_folder.mkdir(parents=True, exist_ok=True)
# magic to fix the GPUs
setup_gpus()
# input data
processed_path = Path("data_proc") / f"{hypa['dataset']}"
data, labels = load_processed(processed_path, words)
# from hypa extract model param
model_param = {}
model_param["num_labels"] = len(words)
model_param["input_shape"] = data["training"][0].shape
model_param["base_filters"] = hypa["base_filters"]
model_param["base_dense_width"] = hypa["base_dense_width"]
# translate types to actual values
kernel_size_types = {
"01": [(2, 2), (2, 2), (2, 2)],
"02": [(5, 1), (3, 3), (3, 3)],
"03": [(1, 5), (3, 3), (3, 3)],
}
model_param["kernel_sizes"] = kernel_size_types[hypa["kernel_size_type"]]
pool_size_types = {
"01": [(2, 2), (2, 2), (2, 2)],
"02": [(2, 1), (2, 2), (2, 2)],
"03": [(1, 2), (2, 2), (2, 2)],
}
model_param["pool_sizes"] = pool_size_types[hypa["pool_size_type"]]
dropout_types = {"01": [0.03, 0.01], "02": [0.3, 0.1]}
model_param["dropouts"] = dropout_types[hypa["dropout_type"]]
# a dict to recreate this training
recap = {}
recap["words"] = words
recap["hypa"] = hypa
recap["model_param"] = model_param
recap["model_name"] = model_name
recap["version"] = "002"
# logg.debug(f"recap: {recap}")
recap_path = info_folder / "recap.json"
recap_path.write_text(json.dumps(recap, indent=4))
learning_rate_types = {
"01": "fixed01",
"02": "fixed02",
"03": "fixed03",
"e1": "exp_decay_keras_01",
"04": "exp_decay_step_01",
"05": "exp_decay_smooth_01",
"06": "exp_decay_smooth_02",
}
learning_rate_type = hypa["learning_rate_type"]
lr_value = learning_rate_types[learning_rate_type]
# setup opt fixed lr values
if lr_value.startswith("fixed"):
if lr_value == "fixed01":
lr = 1e-2
elif lr_value == "fixed02":
lr = 1e-3
elif lr_value == "fixed03":
lr = 1e-4
else:
lr = 1e-3
if lr_value == "exp_decay_keras_01":
lr = ExponentialDecay(0.1, decay_steps=100000, decay_rate=0.96, staircase=True)
optimizer_types = {
"a1": Adam(learning_rate=lr),
"r1": RMSprop(learning_rate=lr),
}
opt = optimizer_types[hypa["optimizer_type"]]
# create the model
model = CNNmodel(**model_param)
# model.summary()
metrics = [
tf.keras.metrics.CategoricalAccuracy(),
tf.keras.metrics.Precision(),
tf.keras.metrics.Recall(),
]
model.compile(
optimizer=opt,
loss=tf.keras.losses.CategoricalCrossentropy(),
metrics=metrics,
)
# setup callbacks
callbacks = []
# setup exp decay step / smooth
if lr_value.startswith("exp_decay"):
if lr_value == "exp_decay_step_01":
exp_decay_part = partial(exp_decay_step, epochs_drop=5)
elif lr_value == "exp_decay_smooth_01":
exp_decay_part = partial(exp_decay_smooth, epochs_drop=5)
elif lr_value == "exp_decay_smooth_02":
exp_decay_part = partial(
exp_decay_smooth, epochs_drop=5, initial_lrate=1e-2
)
lrate = LearningRateScheduler(exp_decay_part)
callbacks.append(lrate)
# # setup early stopping
# early_stop = EarlyStopping(
# # monitor="val_categorical_accuracy",
# monitor="val_loss",
# patience=4,
# verbose=1,
# restore_best_weights=True,
# )
# callbacks.append(early_stop)
# get training parameters
BATCH_SIZE = hypa["batch_size"]
SHUFFLE_BUFFER_SIZE = BATCH_SIZE
EPOCH_NUM = hypa["epoch_num"]
# load the datasets
datasets = {}
for which in ["training", "validation", "testing"]:
# logg.debug(f"data[{which}].shape: {data[which].shape}")
datasets[which] = Dataset.from_tensor_slices((data[which], labels[which]))
# logg.debug(f"datasets[{which}]: {datasets[which]}")
datasets[which] = datasets[which].shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)
# logg.debug(f"datasets[{which}]: {datasets[which]}")
# train the model
results = model.fit(
data["training"],
labels["training"],
# validation_data=datasets["validation"],
validation_data=(data["validation"], labels["validation"]),
batch_size=BATCH_SIZE,
epochs=EPOCH_NUM,
verbose=1,
callbacks=callbacks,
)
# save the trained model
model.save(model_path)
results_recap = {}
results_recap["model_name"] = model_name
# version of the results saved
results_recap["results_recap_version"] = "002"
# quickly evaluate the results
# logg.debug(f"\nmodel.metrics_names: {model.metrics_names}")
# for which in ["training", "validation", "testing"]:
# model_eval = model.evaluate(datasets[which])
# logg.debug(f"{which}: model_eval: {model_eval}")
# save the evaluation results
logg.debug("Evaluate on test data:")
# eval_testing = model.evaluate(datasets["testing"])
# results_recap[model.metrics_names[0]] = eval_testing[0]
# results_recap[model.metrics_names[1]] = eval_testing[1]
eval_testing = model.evaluate(data["testing"], labels["testing"])
for metrics_name, value in zip(model.metrics_names, eval_testing):
logg.debug(f"{metrics_name}: {value}")
results_recap[metrics_name] = value
# compute the confusion matrix
# y_pred = model.predict(datasets["testing"])
y_pred = model.predict(data["testing"])
cm = pred_hot_2_cm(labels["testing"], y_pred, words)
# logg.debug(f"cm: {cm}")
results_recap["cm"] = cm.tolist()
# compute the fscore
fscore = analyze_confusion(cm, words)
logg.debug(f"fscore: {fscore}")
# plot the cm
fig, ax = plt.subplots(figsize=(12, 12))
plot_confusion_matrix(cm, ax, model_name, words, fscore)
plot_cm_path = info_folder / "test_confusion_matrix.png"
fig.savefig(plot_cm_path)
plt.close(fig)
# save the histories
results_recap["history"] = {
"loss": results.history["loss"],
"val_loss": results.history["val_loss"],
"categorical_accuracy": results.history["categorical_accuracy"],
"val_categorical_accuracy": results.history["val_categorical_accuracy"],
}
# save the results
res_recap_path = info_folder / "results_recap.json"
res_recap_path.write_text(json.dumps(results_recap, indent=4))
y_pred_dataset = model.predict(datasets["testing"])
cm_dataset = pred_hot_2_cm(labels["testing"], y_pred_dataset, words)
fscore_dataset = analyze_confusion(cm_dataset, words)
logg.debug(f"fscore_dataset: {fscore_dataset} fscore {fscore}")
# for i, (ys, yd) in enumerate(zip(y_pred, y_pred_dataset)):
# pred_split = np.argmax(ys)
# pred_dataset = np.argmax(yd)
# logg.debug(f"i: {i} pred_split: {pred_split} pred_dataset: {pred_dataset}")
# plt.show()
placeholder_path.write_text(f"Trained. F-score: {fscore}")
return "done_training"
def train_attention(hypa: ty.Dict[str, str], force_retrain: bool,
use_validation: bool) -> None:
"""MAKEDOC: what is train_attention doing?"""
logg = logging.getLogger(f"c.{__name__}.train_attention")
# logg.setLevel("INFO")
logg.debug("Start train_attention")
# build the model name
model_name = build_attention_name(hypa, use_validation)
logg.debug(f"model_name: {model_name}")
# save the trained model here
model_folder = Path("trained_models") / "attention"
if not model_folder.exists():
model_folder.mkdir(parents=True, exist_ok=True)
model_path = model_folder / f"{model_name}.h5"
placeholder_path = model_folder / f"{model_name}.txt"
# check if this model has already been trained
if placeholder_path.exists():
if force_retrain:
logg.warn("\nRETRAINING MODEL!!\n")
else:
logg.debug("Already trained")
return
# save info regarding the model training in this folder
info_folder = Path("info") / "attention" / model_name
if not info_folder.exists():
info_folder.mkdir(parents=True, exist_ok=True)
# get the word list
words = words_types[hypa["words_type"]]
num_labels = len(words)
# load data
processed_folder = Path("data_proc")
processed_path = processed_folder / f"{hypa['dataset_name']}"
data, labels = load_processed(processed_path, words)
# concatenate train and val for final train
val_data = None
if use_validation:
x = data["training"]
y = labels["training"]
val_data = (data["validation"], labels["validation"])
logg.debug("Using validation data")
else:
x = np.concatenate((data["training"], data["validation"]))
y = np.concatenate((labels["training"], labels["validation"]))
logg.debug("NOT using validation data")
# the shape of each sample
input_shape = data["training"][0].shape
# from hypa extract model param
model_param = get_model_param_attention(hypa, num_labels, input_shape)
batch_size_types = {"01": 32, "02": 16}
batch_size = batch_size_types[hypa["batch_size_type"]]
epoch_num_types = {"01": 15, "02": 30, "03": 2, "04": 4}
epoch_num = epoch_num_types[hypa["epoch_num_type"]]
# magic to fix the GPUs
setup_gpus()
model = AttentionModel(**model_param)
# model.summary()
metrics = [
tf.keras.metrics.CategoricalAccuracy(),
tf.keras.metrics.Precision(),
tf.keras.metrics.Recall(),
]
learning_rate_types = {
"01": "fixed01",
"02": "fixed02",
"03": "exp_decay_step_01",
"04": "exp_decay_smooth_01",
"05": "clr_triangular2_01",
"06": "clr_triangular2_02",
"07": "clr_triangular2_03",
"08": "clr_triangular2_04",
"09": "clr_triangular2_05",
"10": "exp_decay_smooth_02",
}
learning_rate_type = hypa["learning_rate_type"]
lr_value = learning_rate_types[learning_rate_type]
# setup opt fixed lr values
if lr_value.startswith("fixed"):
if lr_value == "fixed01":
lr = 1e-3
elif lr_value == "fixed02":
lr = 1e-4
else:
lr = 1e-3
optimizer_types = {
"a1": Adam(learning_rate=lr),
"r1": RMSprop(learning_rate=lr)
}
opt = optimizer_types[hypa["optimizer_type"]]
model.compile(
optimizer=opt,
loss=tf.keras.losses.CategoricalCrossentropy(),
metrics=metrics,
)
# setup callbacks
callbacks = []
# setup exp decay step / smooth
if lr_value.startswith("exp_decay"):
if lr_value == "exp_decay_step_01":
exp_decay_part = partial(exp_decay_step, epochs_drop=5)
elif lr_value == "exp_decay_smooth_01":
exp_decay_part = partial(exp_decay_smooth, epochs_drop=5)
elif lr_value == "exp_decay_smooth_02":
exp_decay_part = partial(exp_decay_smooth,
epochs_drop=5,
initial_lrate=1e-2)
lrate = LearningRateScheduler(exp_decay_part)
callbacks.append(lrate)
# setup cyclic learning rate
if lr_value.startswith("clr_triangular2"):
base_lr = 1e-5
max_lr = 1e-3
# training iteration per epoch = num samples // batch size
# step size suggested = 2~8 * iterations
if lr_value == "clr_triangular2_01":
step_factor = 8
step_size = step_factor * x.shape[0] // batch_size
elif lr_value == "clr_triangular2_02":
step_factor = 2
step_size = step_factor * x.shape[0] // batch_size
# target_cycles = the number of cycles we want in those epochs
# it_per_epoch = num_samples // batch_size
# total_iterations = it_per_epoch * epoch_num
# step_size = total_iterations // target_cycles
elif lr_value == "clr_triangular2_03":
# the number of cycles we want in those epochs
target_cycles = 4
it_per_epoch = x.shape[0] // batch_size
total_iterations = it_per_epoch * epoch_num
step_size = total_iterations // (target_cycles * 2)
elif lr_value == "clr_triangular2_04":
# the number of cycles we want in those epochs
target_cycles = 2
it_per_epoch = x.shape[0] // batch_size
total_iterations = it_per_epoch * epoch_num
step_size = total_iterations // (target_cycles * 2)
elif lr_value == "clr_triangular2_05":
# the number of cycles we want in those epochs
target_cycles = 2
it_per_epoch = x.shape[0] // batch_size
total_iterations = it_per_epoch * epoch_num
step_size = total_iterations // (target_cycles * 2)
# set bigger starting value
max_lr = 1e-2
logg.debug(f"x.shape[0]: {x.shape[0]}")
logg.debug(f"CLR is using step_size: {step_size}")
mode = "triangular2"
cyclic_lr = CyclicLR(base_lr, max_lr, step_size, mode)
callbacks.append(cyclic_lr)
# setup early stopping
if learning_rate_type in ["01", "02", "03", "04"]:
metric_to_monitor = "val_loss" if use_validation else "loss"
early_stop = EarlyStopping(
monitor=metric_to_monitor,
patience=4,
restore_best_weights=True,
verbose=1,
)
callbacks.append(early_stop)
# model_checkpoint = ModelCheckpoint(
# model_name,
# monitor="val_loss",
# save_best_only=True,
# )
# a dict to recreate this training
# FIXME this should be right before fit and have epoch_num/batch_size/lr info
recap: ty.Dict[str, ty.Any] = {}
recap["words"] = words
recap["hypa"] = hypa
recap["model_param"] = model_param
recap["use_validation"] = use_validation
recap["model_name"] = model_name
recap["version"] = "001"
# logg.debug(f"recap: {recap}")
recap_path = info_folder / "recap.json"
recap_path.write_text(json.dumps(recap, indent=4))
results = model.fit(
x,
y,
validation_data=val_data,
epochs=epoch_num,
batch_size=batch_size,
callbacks=callbacks,
)
results_recap: ty.Dict[str, ty.Any] = {}
results_recap["model_name"] = model_name
results_recap["results_recap_version"] = "002"
# eval performance on the various metrics
eval_testing = model.evaluate(data["testing"], labels["testing"])
for metrics_name, value in zip(model.metrics_names, eval_testing):
logg.debug(f"{metrics_name}: {value}")
results_recap[metrics_name] = value
# compute the confusion matrix
y_pred = model.predict(data["testing"])
cm = pred_hot_2_cm(labels["testing"], y_pred, words)
# logg.debug(f"cm: {cm}")
results_recap["cm"] = cm.tolist()
# compute the fscore
fscore = analyze_confusion(cm, words)
logg.debug(f"fscore: {fscore}")
results_recap["fscore"] = fscore
# save the histories
results_recap["history_train"] = {
mn: results.history[mn]
for mn in model.metrics_names
}
if use_validation:
results_recap["history_val"] = {
f"val_{mn}": results.history[f"val_{mn}"]
for mn in model.metrics_names
}
# plot the cm
fig, ax = plt.subplots(figsize=(12, 12))
plot_confusion_matrix(cm, ax, model_name, words, fscore)
plot_cm_path = info_folder / "test_confusion_matrix.png"
fig.savefig(plot_cm_path)
plt.close(fig)
# save the results
res_recap_path = info_folder / "results_recap.json"
res_recap_path.write_text(json.dumps(results_recap, indent=4))
# if cyclic_lr was used save the history
if lr_value.startswith("clr_triangular2"):
logg.debug(f"cyclic_lr.history.keys(): {cyclic_lr.history.keys()}")
clr_recap = {}
for metric_name, values in cyclic_lr.history.items():
clr_recap[metric_name] = list(float(v) for v in values)
clr_recap_path = info_folder / "clr_recap.json"
clr_recap_path.write_text(json.dumps(clr_recap, indent=4))
# save the trained model
model.save(model_path)
placeholder_path.write_text(f"Trained. F-score: {fscore}")
def recompute_fscore_cnn() -> None:
"""MAKEDOC: what is recompute_fscore_cnn doing?"""
logg = logging.getLogger(f"c.{__name__}.recompute_fscore_cnn")
# logg.setLevel("INFO")
logg.debug("Start recompute_fscore_cnn")
info_folder = Path("info")
trained_folder = Path("trained_models")
for model_folder in info_folder.iterdir():
# logg.debug(f"model_folder: {model_folder}")
# check that it is a CNN
model_name = model_folder.name
if not model_name.startswith("CNN"):
continue
# check that the model is trained and not a placeholder
model_path = trained_folder / f"{model_name}.h5"
found_model = False
if model_path.exists():
if model_path.stat().st_size > 100:
found_model = True
if not found_model:
continue
# load it
model = models.load_model(model_path)
res_recap_path = model_folder / "results_recap.json"
if not res_recap_path.exists():
continue
results_recap = json.loads(res_recap_path.read_text())
# logg.debug(f"results_recap['cm']: {results_recap['cm']}")
recap_path = model_folder / "recap.json"
recap = json.loads(recap_path.read_text())
# logg.debug(f"recap['words']: {recap['words']}")
words = recap["words"]
hypa = recap["hypa"]
# check that the data is available
dn = hypa["dataset"]
wt = hypa["words"]
if dn.startswith("mel") or dn.startswith("mfcc"):
preprocess_spec(dn, wt)
elif dn.startswith("aug"):
do_augmentation(dn, wt)
processed_path = Path("data_proc") / f"{hypa['dataset']}"
data, labels = load_processed(processed_path, words)
y_pred = model.predict(data["testing"])
cm = pred_hot_2_cm(labels["testing"], y_pred, words)
fscore = analyze_confusion(cm, words)
# logg.debug(f"fscore: {fscore}")
# overwrite the cm
results_recap["cm"] = cm.tolist()
# add the fscore
results_recap["fscore"] = fscore
# increase the version
results_recap["results_recap_version"] = "002"
# write the new results
res_recap_path.write_text(json.dumps(results_recap, indent=4))
# increase the recap version (shows that it is after this debacle)
recap["version"] = "002"
recap_path.write_text(json.dumps(recap, indent=4))
# save the new plots
fig, ax = plt.subplots(figsize=(12, 12))
plot_confusion_matrix(cm, ax, model_name, words, fscore)
plot_cm_path = info_folder / "test_confusion_matrix.png"
fig.savefig(plot_cm_path)
plt.close(fig)
def delete_bad_models_cnn(args) -> None:
"""MAKEDOC: what is delete_bad_models_cnn doing?"""
logg = logging.getLogger(f"c.{__name__}.delete_bad_models_cnn")
# logg.setLevel("INFO")
logg.debug("Start delete_bad_models_cnn")
info_folder = Path("info") / "cnn"
trained_folder = Path("trained_models") / "cnn"
deleted = 0
recreated = 0
bad_models = 0
good_models = 0
for model_folder in info_folder.iterdir():
# logg.debug(f"model_folder: {model_folder}")
model_name = model_folder.name
if not model_name.startswith("CNN"):
continue
model_name = model_folder.name
model_path = trained_folder / f"{model_name}.h5"
placeholder_path = trained_folder / f"{model_name}.txt"
res_recap_path = model_folder / "results_recap.json"
if not res_recap_path.exists():
logg.warn(f"Skipping {res_recap_path}, not found")
continue
results_recap = json.loads(res_recap_path.read_text())
recap_path = model_folder / "recap.json"
recap = json.loads(recap_path.read_text())
# load info
words_type = recap["hypa"]["words"]
words = recap["words"]
cm = np.array(results_recap["cm"])
fscore = analyze_confusion(cm, words)
# logg.debug(f"fscore: {fscore}")
categorical_accuracy = results_recap["categorical_accuracy"]
# logg.debug(f"categorical_accuracy: {categorical_accuracy}")
if "all" in words_type:
f_tresh = 0.9
ca_tresh = 0.9
elif "f1" in words_type:
f_tresh = 0.975
ca_tresh = 0.975
elif "f2" in words_type:
f_tresh = 0.97
ca_tresh = 0.97
elif "dir" in words_type:
f_tresh = 0.97
ca_tresh = 0.97
elif "num" in words_type:
f_tresh = 0.965
ca_tresh = 0.965
elif "k1" in words_type:
f_tresh = 0.9
ca_tresh = 0.9
elif "w2" in words_type:
f_tresh = 0.85
ca_tresh = 0.85
else:
logg.warn(f"Not specified f_tresh for words_type: {words_type}")
f_tresh = 0.8
ca_tresh = 0.8
if fscore < f_tresh and categorical_accuracy < ca_tresh:
bad_models += 1
if model_path.exists():
# manually uncomment this when ready
# model_path.unlink()
deleted += 1
logg.debug(f"Deleting model_path: {model_path}")
logg.debug(f"\tfscore: {fscore}")
logg.debug(f"\tcategorical_accuracy: {categorical_accuracy}")
# check that a placeholder is there, you have info for this model
else:
if not placeholder_path.exists():
placeholder_path.write_text("Deleted")
logg.debug(
f"Recreating placeholder_path: {placeholder_path}")
recreated += 1
else:
# logg.debug(f"Good model_path {model_path} {words_type}")
# logg.debug(f"\tfscore: {fscore}")
# logg.debug(f"\tcategorical_accuracy: {categorical_accuracy}")
good_models += 1
logg.debug(f"bad_models: {bad_models}")
logg.debug(f"good_models: {good_models}")
logg.debug(f"deleted: {deleted}")
logg.debug(f"recreated: {recreated}")
def test_audio_generator(words_type: str) -> None:
"""MAKEDOC: what is test_audio_generator doing?"""
logg = logging.getLogger(f"c.{__name__}.test_audio_generator")
# logg.setLevel("INFO")
logg.debug("Start test_audio_generator")
partition, ids2labels = prepare_partitions(words_type)
for fold in partition:
logg.debug(f"partition[{fold}][:4]: {partition[fold][:4]}")
logg.debug(f"\nlen(ids2labels): {len(ids2labels)}")
for ID in ids2labels:
logg.debug(f"ids2labels[{ID}]: {ids2labels[ID]}")
break
words = words_types[words_type]
processed_folder = Path("data_split") / "mel04"
data_split_paths = [processed_folder]
params = {
"dim": (64, 64),
"batch_size": 32,
"shuffle": True,
"label_names": words,
"data_split_paths": data_split_paths,
}
training_generator = AudioGenerator(partition["training"], ids2labels, **params)
logg.debug(f"len(training_generator): {len(training_generator)}")
val_generator = AudioGenerator(partition["validation"], ids2labels, **params)
logg.debug(f"len(val_generator): {len(val_generator)}")
# do not shuffle the test data
params["shuffle"] = False
# do not batch it, no loss of stray data at the end
params["batch_size"] = 1
testing_generator = AudioGenerator(partition["testing"], ids2labels, **params)
logg.debug(f"len(testing_generator): {len(testing_generator)}")
X, y = training_generator[0]
logg.debug(f"X.shape: {X.shape} y.shape: {y.shape}")
model_param: ty.Dict[str, ty.Any] = {}
model_param["num_labels"] = len(words)
model_param["input_shape"] = (64, 64, 1)
model_param["base_dense_width"] = 32
model_param["base_filters"] = 20
model_param["dropouts"] = [0.03, 0.01]
model_param["kernel_sizes"] = [(5, 1), (3, 3), (3, 3)]
model_param["pool_sizes"] = [(2, 1), (2, 2), (2, 2)]
model = CNNmodel(**model_param)
# model.summary()
metrics = [
tf.keras.metrics.CategoricalAccuracy(),
tf.keras.metrics.Precision(),
tf.keras.metrics.Recall(),
]
opt = tf.optimizers.Adam()
loss = tf.keras.losses.CategoricalCrossentropy()
model.compile(optimizer=opt, loss=loss, metrics=metrics)
EPOCH_NUM = 5
model.fit(training_generator, validation_data=val_generator, epochs=EPOCH_NUM)
eval_testing = model.evaluate(testing_generator)
for metrics_name, value in zip(model.metrics_names, eval_testing):
logg.debug(f"{metrics_name}: {value}")
y_pred = model.predict(testing_generator)
y_pred_labels = testing_generator.pred2labelnames(y_pred)
y_true = testing_generator.get_true_labels()
cm = confusion_matrix(y_true, y_pred_labels)
fscore = analyze_confusion(cm, words)
logg.debug(f"fscore: {fscore}")
fig, ax = plt.subplots(figsize=(12, 12))
plot_confusion_matrix(cm, ax, "Test generator", words, fscore)
fig.tight_layout()
plt.show()
def evaluate_model_cnn(which_dataset: str, train_words_type: str,
test_words_type: str) -> None:
"""MAKEDOC: what is evaluate_model_cnn doing?"""
logg = logging.getLogger(f"c.{__name__}.evaluate_model_cnn")
# logg.setLevel("INFO")
logg.debug("Start evaluate_model_cnn")
# magic to fix the GPUs
setup_gpus()
# setup the parameters
# hypa: ty.Dict[str, ty.Union[str, int]] = {}
# hypa["base_dense_width"] = 32
# hypa["base_filters"] = 20
# hypa["batch_size"] = 32
# hypa["dropout_type"] = "01"
# # hypa["epoch_num"] = 16
# hypa["epoch_num"] = 15
# hypa["kernel_size_type"] = "02"
# # hypa["pool_size_type"] = "02"
# hypa["pool_size_type"] = "01"
# # hypa["learning_rate_type"] = "02"
# hypa["learning_rate_type"] = "04"
# hypa["optimizer_type"] = "a1"
# hypa["dataset"] = which_dataset
# hypa["words"] = train_words_type
# hypa: ty.Dict[str, ty.Union[str, int]] = {}
# hypa["base_dense_width"] = 32
# hypa["base_filters"] = 32
# hypa["batch_size"] = 32
# hypa["dropout_type"] = "02"
# hypa["epoch_num"] = 15
# hypa["kernel_size_type"] = "02"
# hypa["pool_size_type"] = "01"
# hypa["learning_rate_type"] = "04"
# hypa["optimizer_type"] = "a1"
# hypa["dataset"] = which_dataset
# hypa["words"] = train_words_type
hypa: ty.Dict[str, ty.Union[str, int]] = {
"base_dense_width": 32,
"base_filters": 32,
"batch_size": 32,
# "dataset": "aug07",
"dropout_type": "01",
"epoch_num": 15,
"kernel_size_type": "02",
"learning_rate_type": "04",
"optimizer_type": "a1",
"pool_size_type": "01",
# "words": "all",
}
hypa["dataset"] = which_dataset
hypa["words"] = train_words_type
# get the words
# train_words = words_types[train_words_type]
test_words = words_types[test_words_type]
model_name = build_cnn_name(hypa)
logg.debug(f"model_name: {model_name}")
model_folder = Path("trained_models") / "cnn"
model_path = model_folder / f"{model_name}.h5"
if not model_path.exists():
logg.error(f"Model not found at: {model_path}")
raise FileNotFoundError
model = tf.keras.models.load_model(model_path)
model.summary()
# input data
processed_path = Path("data_proc") / f"{which_dataset}"
data, labels = load_processed(processed_path, test_words)
logg.debug(f"data['testing'].shape: {data['testing'].shape}")
# evaluate on the words you trained on
logg.debug("Evaluate on test data:")
model.evaluate(data["testing"], labels["testing"])
# model.evaluate(data["validation"], labels["validation"])
# predict labels/cm/fscore
y_pred = model.predict(data["testing"])
cm = pred_hot_2_cm(labels["testing"], y_pred, test_words)
# y_pred = model.predict(data["validation"])
# cm = pred_hot_2_cm(labels["validation"], y_pred, test_words)
fscore = analyze_confusion(cm, test_words)
logg.debug(f"fscore: {fscore}")
fig, ax = plt.subplots(figsize=(12, 12))
plot_confusion_matrix(cm, ax, model_name, test_words, fscore)
plt.show()
def build_cnn_results_df() -> pd.DataFrame:
"""MAKEDOC: what is build_cnn_results_df doing?"""
logg = logging.getLogger(f"c.{__name__}.build_cnn_results_df")
logg.setLevel("INFO")
logg.debug("Start build_cnn_results_df")
info_folder = Path("info") / "cnn"
pandito: ty.Dict[str, ty.List[str]] = {
"dense_width": [],
"filters": [],
"batch_size": [],
"dataset": [],
"dropout": [],
"epoch_num": [],
"kernel_size": [],
"pool_size": [],
"lr": [],
"opt": [],
"words": [],
"fscore": [],
"loss": [],
"cat_acc": [],
"model_name": [],
}
for model_folder in info_folder.iterdir():
logg.debug(f"model_folder: {model_folder}")
model_name = model_folder.name
if not model_name.startswith("CNN"):
continue
res_recap_path = model_folder / "results_recap.json"
if not res_recap_path.exists():
continue
results_recap = json.loads(res_recap_path.read_text())
logg.debug(f"results_recap['cm']: {results_recap['cm']}")
recap_path = model_folder / "recap.json"
recap = json.loads(recap_path.read_text())
logg.debug(f"recap['words']: {recap['words']}")
cm = np.array(results_recap["cm"])
fscore = analyze_confusion(cm, recap["words"])
logg.debug(f"fscore: {fscore}")
categorical_accuracy = results_recap["categorical_accuracy"]
logg.debug(f"categorical_accuracy: {categorical_accuracy}")
pandito["dense_width"].append(recap["hypa"]["base_dense_width"])
pandito["filters"].append(recap["hypa"]["base_filters"])
pandito["batch_size"].append(recap["hypa"]["batch_size"])
pandito["dataset"].append(recap["hypa"]["dataset"])
pandito["dropout"].append(recap["hypa"]["dropout_type"])
pandito["epoch_num"].append(recap["hypa"]["epoch_num"])
pandito["kernel_size"].append(recap["hypa"]["kernel_size_type"])
pandito["pool_size"].append(recap["hypa"]["pool_size_type"])
pandito["words"].append(recap["hypa"]["words"])
pandito["cat_acc"].append(results_recap["categorical_accuracy"])
pandito["loss"].append(results_recap["loss"])
pandito["model_name"].append(results_recap["model_name"])
# pandito["fscore"].append(fscore)
pandito["fscore"].append(categorical_accuracy)
if "version" in recap:
if recap["version"] == "001":
pandito["lr"].append(recap["hypa"]["learning_rate_type"])
pandito["opt"].append(recap["hypa"]["optimizer_type"])
elif recap["version"] == "002":
if "learning_rate_type" in recap["hypa"]:
pandito["lr"].append(recap["hypa"]["learning_rate_type"])
pandito["opt"].append(recap["hypa"]["optimizer_type"])
else:
# pandito["lr"].append("default")
# pandito["opt"].append("adam")
pandito["lr"].append("01")
pandito["opt"].append("a1")
else:
# pandito["lr"].append("default")
# pandito["opt"].append("adam")
pandito["lr"].append("01")
pandito["opt"].append("a1")
df = pd.DataFrame(pandito)
return df
def train_img(
hypa: ty.Dict[str, str],
force_retrain: bool,
use_validation: bool,
trained_folder: Path,
root_info_folder: Path,
) -> None:
"""MAKEDOC: what is train_img doing?"""
logg = logging.getLogger(f"c.{__name__}.train_img")
# logg.setLevel("INFO")
logg.debug("Start train_img")
##########################################################
# Setup folders
##########################################################
# name the model
model_name = build_img_name(hypa, use_validation)
logg.debug(f"model_name: {model_name}")
# save the trained model here
model_path = trained_folder / f"{model_name}.h5"
placeholder_path = trained_folder / f"{model_name}.txt"
# check if this model has already been trained
if placeholder_path.exists():
if force_retrain:
logg.warn("\nRETRAINING MODEL!!\n")
else:
logg.debug("Already trained")
return
# save info regarding the model training in this folder
model_info_folder = root_info_folder / model_name
if not model_info_folder.exists():
model_info_folder.mkdir(parents=True, exist_ok=True)
# magic to fix the GPUs
setup_gpus()
##########################################################
# Load data
##########################################################
label_type = hypa["words_type"]
label_list = get_label_list(label_type)
num_labels = len(label_list)
dataset_raw_folder = Path.home(
) / "datasets" / "imagenet" / "imagenet_images"
dataset_proc_base_folder = Path.home() / "datasets" / "imagenet"
# get the partition of the data
partition, ids2labels = prepare_partitions(label_list, dataset_raw_folder)
num_samples = len(partition["training"])
# from hypa extract training param (epochs, batch, opt, ...)
training_param = get_training_param_img(hypa, use_validation, model_path,
num_samples)
preprocess_type = hypa["dataset_name"]
dataset_proc_folder = dataset_proc_base_folder / preprocess_type
val_generator: ty.Optional[ImageNetGenerator] = None
if use_validation:
val_generator = ImageNetGenerator(
partition["validation"],
ids2labels,
label_list,
dataset_proc_folder=dataset_proc_folder,
dataset_raw_folder=dataset_raw_folder,
preprocess_type=preprocess_type,
save_processed=True,
batch_size=training_param["batch_size"],
shuffle=True,
)
logg.debug("Using validation data")
else:
partition["training"].extend(partition["validation"])
logg.debug("NOT using validation data")
training_generator = ImageNetGenerator(
partition["training"],
ids2labels,
label_list,
dataset_proc_folder=dataset_proc_folder,
dataset_raw_folder=dataset_raw_folder,
preprocess_type=preprocess_type,
save_processed=True,
batch_size=training_param["batch_size"],
shuffle=True,
)
testing_generator = ImageNetGenerator(
partition["testing"],
ids2labels,
label_list,
dataset_proc_folder=dataset_proc_folder,
dataset_raw_folder=dataset_raw_folder,
preprocess_type=preprocess_type,
save_processed=True,
batch_size=1,
shuffle=False,
)
##########################################################
# Setup model
##########################################################
input_shape = training_generator.get_img_shape()
# from hypa extract model param
model_param = get_model_param_img(hypa, num_labels, input_shape)
# get the model with the chosen params
net_type = hypa["net_type"]
if net_type == "ARN":
model = AreaNet.build(**model_param)
elif net_type == "AAN":
model = ActualAreaNet.build(**model_param)
elif net_type == "VAN":
model = VerticalAreaNet.build(**model_param)
elif net_type.startswith("SI"):
if net_type == "SIM":
sim_type = "1"
elif net_type == "SI2":
sim_type = "2"
model = SimpleNet.build(sim_type=sim_type, **model_param)
# a few metrics to track
metrics = [
tf.keras.metrics.CategoricalAccuracy(),
tf.keras.metrics.Precision(),
tf.keras.metrics.Recall(),
]
# compile the model
model.compile(
optimizer=training_param["opt"],
loss=tf.keras.losses.CategoricalCrossentropy(),
metrics=metrics,
)
# recap
recap: ty.Dict[str, ty.Any] = {}
recap["model_name"] = model_name
recap["words"] = label_list
recap["hypa"] = hypa
recap["model_param"] = model_param
recap["use_validation"] = use_validation
recap["batch_size"] = training_param["batch_size"]
recap["epochs"] = training_param["epochs"]
recap["lr_name"] = training_param["lr_name"]
recap["version"] = "002"
# logg.debug(f"recap: {recap}")
recap_path = model_info_folder / "recap.json"
recap_path.write_text(json.dumps(recap, indent=4))
# https://stackoverflow.com/a/45546663/2237151
model_summary_path = model_info_folder / "model_summary.txt"
with model_summary_path.open("w") as msf:
model.summary(line_length=150, print_fn=lambda x: msf.write(x + "\n"))
##########################################################
# Fit model
##########################################################
results = model.fit(
training_generator,
validation_data=val_generator,
epochs=training_param["epochs"],
batch_size=training_param["batch_size"],
callbacks=training_param["callbacks"],
)
##########################################################
# Save results, history, performance
##########################################################
# results_recap
results_recap: ty.Dict[str, ty.Any] = {}
results_recap["model_name"] = model_name
results_recap["results_recap_version"] = "001"
# evaluate performance
eval_testing = model.evaluate(testing_generator)
for metrics_name, value in zip(model.metrics_names, eval_testing):
logg.debug(f"{metrics_name}: {value}")
results_recap[metrics_name] = value
# confusion matrix
y_pred = model.predict(testing_generator)
y_pred_labels = testing_generator.pred2labelnames(y_pred)
y_true = testing_generator.get_true_labels()
cm = confusion_matrix(y_true, y_pred_labels)
results_recap["cm"] = cm.tolist()
# fscore
fscore = analyze_confusion(cm, label_list)
logg.debug(f"fscore: {fscore}")
results_recap["fscore"] = fscore
# save the histories
results_recap["history_train"] = {
mn: results.history[mn]
for mn in model.metrics_names
}
if use_validation:
results_recap["history_val"] = {
f"val_{mn}": results.history[f"val_{mn}"]
for mn in model.metrics_names
}
# save the results
res_recap_path = model_info_folder / "results_recap.json"
res_recap_path.write_text(json.dumps(results_recap, indent=4))
# plot the cm
fig, ax = plt.subplots(figsize=(12, 12))
plot_confusion_matrix(cm, ax, model_name, label_list, fscore)
plot_cm_path = model_info_folder / "test_confusion_matrix.png"
fig.savefig(plot_cm_path)
plt.close(fig)
# save the trained model
model.save(model_path)
# save the placeholder
placeholder_path.write_text(f"Trained. F-score: {fscore}")
def evaluate_model_cm(model_name: str, test_words_type: str) -> float:
r"""MAKEDOC: what is evaluate_model_cm doing?"""
logg = logging.getLogger(f"c.{__name__}.evaluate_model_cm")
# logg.setLevel("INFO")
# logg.debug("\nStart evaluate_model_cm")
# magic to fix the GPUs
setup_gpus()
logg.debug(f"\nmodel_name: {model_name}")
dataset_re = re.compile("_ds(.*?)_")
match = dataset_re.search(model_name)
if match is not None:
logg.debug(f"match[1]: {match[1]}")
dataset_name = match[1]
train_words_type_re = re.compile("_w(.*?)[_.]")
match = train_words_type_re.search(model_name)
if match is not None:
logg.debug(f"match[1]: {match[1]}")
train_words_type = match[1]
arch_type = model_name[:3]
if arch_type == "ATT":
train_type_tag = "attention"
else:
train_type_tag = "area"
# load the model
model_folder = Path("trained_models") / train_type_tag
model_path = model_folder / f"{model_name}.h5"
model = tf_models.load_model(model_path)
# model.summary()
train_words = words_types[train_words_type]
logg.debug(f"train_words: {train_words}")
test_words = words_types[test_words_type]
logg.debug(f"test_words: {test_words}")
# input data must exist
if dataset_name.startswith("mel"):
preprocess_spec(dataset_name, test_words_type)
elif dataset_name.startswith("aug"):
do_augmentation(dataset_name, test_words_type)
# input data
processed_path = Path("data_proc") / f"{dataset_name}"
data, labels = load_processed(processed_path, test_words)
logg.debug(f"list(data.keys()): {list(data.keys())}")
logg.debug(f"data['testing'].shape: {data['testing'].shape}")
# evaluate on the words you trained on
logg.debug("Evaluate on test data:")
model.evaluate(data["testing"], labels["testing"])
# model.evaluate(data["validation"], labels["validation"])
# predict labels/cm/fscore
y_pred = model.predict(data["testing"])
cm = pred_hot_2_cm(labels["testing"], y_pred, test_words)
# y_pred = model.predict(data["validation"])
# cm = pred_hot_2_cm(labels["validation"], y_pred, test_words)
fscore = analyze_confusion(cm, test_words)
logg.debug(f"fscore: {fscore}")
fig, ax = plt.subplots(figsize=(12, 12))
plot_confusion_matrix(cm, ax, model_name, test_words, fscore, train_words)
fig_name = f"{model_name}_test{test_words_type}_cm.{{}}"
cm_folder = Path("plot_results") / "cm_all01"
if not cm_folder.exists():
cm_folder.mkdir(parents=True, exist_ok=True)
plot_cm_path = cm_folder / fig_name.format("png")
fig.savefig(plot_cm_path)
plot_cm_path = cm_folder / fig_name.format("pdf")
fig.savefig(plot_cm_path)
# plt.show()
return fscore
def train_area(
hypa: ty.Dict[str, str],
force_retrain: bool,
use_validation: bool,
trained_folder: Path,
root_info_folder: Path,
) -> None:
"""MAKEDOC: what is train_area doing?"""
logg = logging.getLogger(f"c.{__name__}.train_area")
# logg.setLevel("INFO")
logg.debug("Start train_area")
##########################################################
# Setup folders
##########################################################
# name the model
model_name = build_area_name(hypa, use_validation)
logg.debug(f"model_name: {model_name}")
# save the trained model here
model_path = trained_folder / f"{model_name}.h5"
placeholder_path = trained_folder / f"{model_name}.txt"
# check if this model has already been trained
if placeholder_path.exists():
if force_retrain:
logg.warn("\nRETRAINING MODEL!!\n")
else:
logg.debug("Already trained")
return
# save info regarding the model training in this folder
model_info_folder = root_info_folder / model_name
if not model_info_folder.exists():
model_info_folder.mkdir(parents=True, exist_ok=True)
# magic to fix the GPUs
setup_gpus()
##########################################################
# Load data
##########################################################
# get the words
words = words_types[hypa["words_type"]]
num_labels = len(words)
# load data
processed_folder = Path("data_proc")
processed_path = processed_folder / f"{hypa['dataset_name']}"
data, labels = load_processed(processed_path, words)
# concatenate train and val for final train
val_data = None
if use_validation:
x = data["training"]
y = labels["training"]
val_data = (data["validation"], labels["validation"])
logg.debug("Using validation data")
else:
x = np.concatenate((data["training"], data["validation"]))
y = np.concatenate((labels["training"], labels["validation"]))
logg.debug("NOT using validation data")
##########################################################
# Setup model
##########################################################
# the shape of each sample
input_shape = data["training"][0].shape
# from hypa extract model param
model_param = get_model_param_area(hypa, num_labels, input_shape)
# get the model with the chosen params
net_type = hypa["net_type"]
if net_type == "ARN":
model = AreaNet.build(**model_param)
elif net_type == "AAN":
model = ActualAreaNet.build(**model_param)
elif net_type == "VAN":
model = VerticalAreaNet.build(**model_param)
elif net_type.startswith("SI"):
if net_type == "SIM":
sim_type = "1"
elif net_type == "SI2":
sim_type = "2"
model = SimpleNet.build(sim_type=sim_type, **model_param)
num_samples = x.shape[0]
logg.debug(f"num_samples: {num_samples}")
# from hypa extract training param (epochs, batch, opt, ...)
training_param = get_training_param_area(hypa, use_validation, model_path,
num_samples)
# a few metrics to track
metrics = [
tf.keras.metrics.CategoricalAccuracy(),
tf.keras.metrics.Precision(),
tf.keras.metrics.Recall(),
]
# compile the model
model.compile(
optimizer=training_param["opt"],
loss=tf.keras.losses.CategoricalCrossentropy(),
metrics=metrics,
)
# recap
recap: ty.Dict[str, ty.Any] = {}
recap["model_name"] = model_name
recap["words"] = words
recap["hypa"] = hypa
recap["model_param"] = model_param
recap["use_validation"] = use_validation
recap["batch_size"] = training_param["batch_size"]
recap["epochs"] = training_param["epochs"]
recap["lr_name"] = training_param["lr_name"]
recap["version"] = "002"
# logg.debug(f"recap: {recap}")
recap_path = model_info_folder / "recap.json"
recap_path.write_text(json.dumps(recap, indent=4))
# https://stackoverflow.com/a/45546663/2237151
model_summary_path = model_info_folder / "model_summary.txt"
with model_summary_path.open("w") as msf:
model.summary(line_length=150, print_fn=lambda x: msf.write(x + "\n"))
##########################################################
# Fit model
##########################################################
results = model.fit(
x,
y,
validation_data=val_data,
epochs=training_param["epochs"],
batch_size=training_param["batch_size"],
callbacks=training_param["callbacks"],
)
##########################################################
# Save results, history, performance
##########################################################
# results_recap
results_recap: ty.Dict[str, ty.Any] = {}
results_recap["model_name"] = model_name
results_recap["results_recap_version"] = "001"
# evaluate performance
eval_testing = model.evaluate(data["testing"], labels["testing"])
for metrics_name, value in zip(model.metrics_names, eval_testing):
logg.debug(f"{metrics_name}: {value}")
results_recap[metrics_name] = value
# confusion matrix
y_pred = model.predict(data["testing"])
cm = pred_hot_2_cm(labels["testing"], y_pred, words)
results_recap["cm"] = cm.tolist()
# fscore
fscore = analyze_confusion(cm, words)
logg.debug(f"fscore: {fscore}")
results_recap["fscore"] = fscore
# save the histories
results_recap["history_train"] = {
mn: results.history[mn]
for mn in model.metrics_names
}
if use_validation:
results_recap["history_val"] = {
f"val_{mn}": results.history[f"val_{mn}"]
for mn in model.metrics_names
}
# save the results
res_recap_path = model_info_folder / "results_recap.json"
res_recap_path.write_text(json.dumps(results_recap, indent=4))
# plot the cm
fig, ax = plt.subplots(figsize=(12, 12))
plot_confusion_matrix(cm, ax, model_name, words, fscore)
plot_cm_path = model_info_folder / "test_confusion_matrix.png"
fig.savefig(plot_cm_path)
plt.close(fig)
# save the trained model
model.save(model_path)
# save the placeholder
placeholder_path.write_text(f"Trained. F-score: {fscore}")
