def to_z_latent(self, adata, batch_key):
"""
Map `adata` in to the latent space. This function will feed data
in encoder part of C-VAE and compute the latent space coordinates
for each sample in data.
# Parameters
adata: `~anndata.AnnData`
Annotated data matrix to be mapped to latent space. `data.X` has to be in shape [n_obs, n_vars].
encoder_labels: numpy nd-array
`numpy nd-array` of labels to be fed as CVAE's condition array.
return_adata: boolean
if `True`, will output as an `anndata` object or put the results in the `obsm` attribute of `adata`
# Returns
output: `~anndata.AnnData`
returns `anndata` object containing latent space encoding of 'adata'
"""
if sparse.issparse(adata.X):
adata.X = adata.X.A
encoder_labels, _ = label_encoder(adata, self.condition_encoder, batch_key)
encoder_labels = to_categorical(encoder_labels, num_classes=self.n_conditions)
latent = self.encoder_model.predict([adata.X, encoder_labels])[2]
latent = np.nan_to_num(latent)
latent_adata = anndata.AnnData(X=latent)
latent_adata.obs = adata.obs.copy(deep=True)
return latent_adata
def get_reconstruction_error(self, adata, condition_key):
adata = remove_sparsity(adata)
labels_encoded, _ = label_encoder(adata, None, condition_key)
labels_onehot = to_categorical(labels_encoded,
num_classes=self.n_conditions)
x = [adata.X, labels_onehot, labels_onehot]
y = [adata.X, labels_encoded]
return self.cvae_model.evaluate(x, y, verbose=0)
def to_mmd_layer(self, adata, batch_key):
"""
Map ``adata`` in to the MMD space. This function will feed data
in ``mmd_model`` of trVAE and compute the MMD space coordinates
for each sample in data.
Parameters
----------
adata: :class:`~anndata.AnnData`
Annotated data matrix to be mapped to MMD latent space.
Please note that ``adata.X`` has to be in shape [n_obs, x_dimension]
encoder_labels: :class:`~numpy.ndarray`
:class:`~numpy.ndarray` of labels to be fed as trVAE'sencoder condition array.
decoder_labels: :class:`~numpy.ndarray`
:class:`~numpy.ndarray` of labels to be fed as trVAE'sdecoder condition array.
Returns
-------
adata_mmd: :class:`~anndata.AnnData`
returns Annotated data containing MMD latent space encoding of ``adata``
"""
adata = remove_sparsity(adata)
encoder_labels, _ = label_encoder(adata, self.condition_encoder, batch_key)
decoder_labels, _ = label_encoder(adata, self.condition_encoder, batch_key)
encoder_labels = to_categorical(encoder_labels, num_classes=self.n_conditions)
decoder_labels = to_categorical(decoder_labels, num_classes=self.n_conditions)
cvae_inputs = [adata.X, encoder_labels, decoder_labels]
mmd = self.cvae_model.predict(cvae_inputs)[1]
mmd = np.nan_to_num(mmd, nan=0.0, posinf=0.0, neginf=0.0)
adata_mmd = anndata.AnnData(X=mmd)
adata_mmd.obs = adata.obs.copy(deep=True)
return adata_mmd
def predict(self, adata, condition_key, target_condition=None):
"""Feeds ``adata`` to trVAE and produces the reconstructed data.
Parameters
----------
adata: :class:`~anndata.AnnData`
Annotated data matrix whether in primary space.
condition_key: str
:class:`~numpy.ndarray` of labels to be fed as trVAE'sencoder condition array.
target_condition: str
:class:`~numpy.ndarray` of labels to be fed as trVAE'sdecoder condition array.
Returns
-------
adata_pred: `~anndata.AnnData`
Annotated data of predicted cells in primary space.
"""
adata = remove_sparsity(adata)
encoder_labels, _ = label_encoder(adata, self.condition_encoder, condition_key)
if target_condition is not None:
decoder_labels = np.zeros_like(encoder_labels) + self.condition_encoder[
target_condition]
else:
decoder_labels, _ = label_encoder(adata, self.condition_encoder, condition_key)
encoder_labels = to_categorical(encoder_labels, num_classes=self.n_conditions)
decoder_labels = to_categorical(decoder_labels, num_classes=self.n_conditions)
x_hat = self.cvae_model.predict([adata.X, encoder_labels, decoder_labels])[0]
adata_pred = anndata.AnnData(X=x_hat)
adata_pred.obs = adata.obs
adata_pred.var_names = adata.var_names
return adata_pred
def create_model(net_train_adata, net_valid_adata, domain_key, label_key,
source_domains, target_domains, domain_encoder):
z_dim_choices = {{choice([20, 40, 50, 60, 80, 100])}}
mmd_dim_choices = {{choice([64, 128, 256])}}
alpha_choices = {{choice([0.1, 0.01, 0.001, 0.0001, 0.00001, 0.000001])}}
beta_choices = {{choice([1, 100, 500, 1000, 1500, 2000, 5000])}}
gamma_choices = {{choice([1, 10, 100, 1000, 5000, 10000])}}
eta_choices = {{choice([0.01, 0.1, 1, 5, 10, 50])}}
batch_size_choices = {{choice([128, 256, 512, 1024, 1500])}}
dropout_rate_choices = {{choice([0.1, 0.2, 0.5])}}
n_labels = len(net_train_adata.obs[label_key].unique().tolist())
n_domains = len(net_train_adata.obs[domain_key].unique().tolist())
network = trvae.archs.trVAEATAC(
x_dimension=net_train_adata.shape[1],
z_dimension=z_dim_choices,
mmd_dimension=mmd_dim_choices,
learning_rate=0.001,
alpha=alpha_choices,
beta=beta_choices,
gamma=gamma_choices,
eta=eta_choices,
model_path=f"./models/trVAEATAC/hyperopt/Pancreas/",
n_labels=n_labels,
n_domains=n_domains,
output_activation='leaky_relu',
mmd_computation_way="1",
dropout_rate=dropout_rate_choices)
network.train(
net_train_adata,
net_valid_adata,
domain_key,
label_key,
source_key=source_domains,
target_key=target_domains,
domain_encoder=domain_encoder,
n_epochs=10000,
batch_size=batch_size_choices,
early_stop_limit=500,
lr_reducer=0,
)
target_adata_train = net_train_adata.copy()[
net_train_adata.obs[domain_key].isin(target_domains)]
target_adata_valid = net_valid_adata.copy()[
net_valid_adata.obs[domain_key].isin(target_domains)]
target_adata = target_adata_train.concatenate(target_adata_valid)
target_adata = remove_sparsity(target_adata)
target_adata_domains_encoded, _ = label_encoder(
target_adata, condition_key=domain_key, label_encoder=domain_encoder)
target_adata_domains_onehot = to_categorical(target_adata_domains_encoded,
num_classes=n_domains)
target_adata_classes_encoded = network.label_enc.transform(
target_adata.obs[label_key].values)
target_adata_classes_onehot = to_categorical(target_adata_classes_encoded,
num_classes=n_labels)
x_target = [
target_adata.X, target_adata_domains_onehot,
target_adata_domains_onehot
]
y_target = target_adata_classes_onehot
_, target_acc = network.classifier_model.evaluate(x_target,
y_target,
verbose=0)
objective = -target_acc
print(
f'alpha = {network.alpha}, beta = {network.beta}, eta={network.eta}, z_dim = {network.z_dim}, mmd_dim = {network.mmd_dim}, batch_size = {batch_size_choices}, dropout_rate = {network.dr_rate}, gamma = {network.gamma}'
)
return {'loss': objective, 'status': STATUS_OK}
def _train_on_batch(self, adata,
condition_key, train_size=0.8,
n_epochs=300, batch_size=512,
early_stop_limit=10, lr_reducer=7,
save=True, retrain=True, verbose=3):
train_adata, valid_adata = train_test_split(adata, train_size)
if self.gene_names is None:
self.gene_names = train_adata.var_names.tolist()
else:
if set(self.gene_names).issubset(set(train_adata.var_names)):
train_adata = train_adata[:, self.gene_names]
else:
raise Exception("set of gene names in train adata are inconsistent with class' gene_names")
if set(self.gene_names).issubset(set(valid_adata.var_names)):
valid_adata = valid_adata[:, self.gene_names]
else:
raise Exception("set of gene names in valid adata are inconsistent with class' gene_names")
train_conditions_encoded, self.condition_encoder = label_encoder(train_adata, le=self.condition_encoder,
condition_key=condition_key)
valid_conditions_encoded, self.condition_encoder = label_encoder(valid_adata, le=self.condition_encoder,
condition_key=condition_key)
if not retrain and os.path.exists(os.path.join(self.model_path, f"{self.model_name}.h5")):
self.restore_model_weights()
return
train_conditions_onehot = to_categorical(train_conditions_encoded, num_classes=self.n_conditions)
valid_conditions_onehot = to_categorical(valid_conditions_encoded, num_classes=self.n_conditions)
if sparse.issparse(train_adata.X):
is_sparse = True
else:
is_sparse = False
train_expr = train_adata.X
valid_expr = valid_adata.X.A if is_sparse else valid_adata.X
x_valid = [valid_expr, valid_conditions_onehot, valid_conditions_onehot]
if self.loss_fn in ['nb', 'zinb']:
x_valid.append(valid_adata.obs[self.size_factor_key].values)
y_valid = [valid_adata.raw.X.A if sparse.issparse(valid_adata.raw.X) else valid_adata.raw.X,
valid_conditions_encoded]
else:
y_valid = [valid_expr, valid_conditions_encoded]
es_patience, best_val_loss = 0, 1e10
for i in range(n_epochs):
train_loss = train_recon_loss = train_mmd_loss = 0.0
for j in range(min(200, train_adata.shape[0] // batch_size)):
batch_indices = np.random.choice(train_adata.shape[0], batch_size)
batch_expr = train_expr[batch_indices, :].A if is_sparse else train_expr[batch_indices, :]
x_train = [batch_expr, train_conditions_onehot[batch_indices], train_conditions_onehot[batch_indices]]
if self.loss_fn in ['nb', 'zinb']:
x_train.append(train_adata.obs[self.size_factor_key].values[batch_indices])
y_train = [train_adata.raw.X[batch_indices].A if sparse.issparse(
train_adata.raw.X[batch_indices]) else train_adata.raw.X[batch_indices],
train_conditions_encoded[batch_indices]]
else:
y_train = [batch_expr, train_conditions_encoded[batch_indices]]
batch_loss, batch_recon_loss, batch_kl_loss = self.cvae_model.train_on_batch(x_train, y_train)
train_loss += batch_loss / batch_size
train_recon_loss += batch_recon_loss / batch_size
train_mmd_loss += batch_kl_loss / batch_size
valid_loss, valid_recon_loss, valid_mmd_loss = self.cvae_model.evaluate(x_valid, y_valid, verbose=0)
if valid_loss < best_val_loss:
best_val_loss = valid_loss
es_patience = 0
else:
es_patience += 1
if es_patience == early_stop_limit:
print("Training stopped with Early Stopping")
break
logs = {"loss": train_loss, "recon_loss": train_recon_loss, "mmd_loss": train_mmd_loss,
"val_loss": valid_loss, "val_recon_loss": valid_recon_loss, "val_mmd_loss": valid_mmd_loss}
print_progress(i, logs, n_epochs)
if save:
self.save(make_dir=True)
def _fit(self, adata,
condition_key, train_size=0.8,
n_epochs=300, batch_size=512,
early_stop_limit=10, lr_reducer=7,
save=True, retrain=True, verbose=3):
train_adata, valid_adata = train_test_split(adata, train_size)
if self.gene_names is None:
self.gene_names = train_adata.var_names.tolist()
else:
if set(self.gene_names).issubset(set(train_adata.var_names)):
train_adata = train_adata[:, self.gene_names]
else:
raise Exception("set of gene names in train adata are inconsistent with class' gene_names")
if set(self.gene_names).issubset(set(valid_adata.var_names)):
valid_adata = valid_adata[:, self.gene_names]
else:
raise Exception("set of gene names in valid adata are inconsistent with class' gene_names")
train_expr = train_adata.X.A if sparse.issparse(train_adata.X) else train_adata.X
valid_expr = valid_adata.X.A if sparse.issparse(valid_adata.X) else valid_adata.X
train_conditions_encoded, self.condition_encoder = label_encoder(train_adata, le=self.condition_encoder,
condition_key=condition_key)
valid_conditions_encoded, self.condition_encoder = label_encoder(valid_adata, le=self.condition_encoder,
condition_key=condition_key)
if not retrain and os.path.exists(os.path.join(self.model_path, f"{self.model_name}.h5")):
self.restore_model_weights()
return
callbacks = [
History(),
]
if verbose > 2:
callbacks.append(
LambdaCallback(on_epoch_end=lambda epoch, logs: print_progress(epoch, logs, n_epochs)))
fit_verbose = 0
else:
fit_verbose = verbose
if early_stop_limit > 0:
callbacks.append(EarlyStopping(patience=early_stop_limit, monitor='val_loss'))
if lr_reducer > 0:
callbacks.append(ReduceLROnPlateau(monitor='val_loss', patience=lr_reducer))
train_conditions_onehot = to_categorical(train_conditions_encoded, num_classes=self.n_conditions)
valid_conditions_onehot = to_categorical(valid_conditions_encoded, num_classes=self.n_conditions)
x_train = [train_expr, train_conditions_onehot, train_conditions_onehot]
x_valid = [valid_expr, valid_conditions_onehot, valid_conditions_onehot]
y_train = [train_expr, train_conditions_encoded]
y_valid = [valid_expr, valid_conditions_encoded]
self.cvae_model.fit(x=x_train,
y=y_train,
validation_data=(x_valid, y_valid),
epochs=n_epochs,
batch_size=batch_size,
verbose=fit_verbose,
callbacks=callbacks,
)
if save:
self.save(make_dir=True)
def train(self,
train_adata,
valid_adata=None,
condition_encoder=None,
condition_key='condition',
n_epochs=10000,
batch_size=1024,
early_stop_limit=100,
lr_reducer=80,
threshold=0.0,
monitor='val_loss',
shuffle=True,
verbose=0,
save=True,
monitor_best=True):
"""
Trains the network `n_epochs` times with given `train_data`
and validates the model using validation_data if it was given
in the constructor function. This function is using `early stopping`
technique to prevent overfitting.
# Parameters
train_adata: `~anndata.AnnData`
`AnnData` object for training trVAE
valid_adata: `~anndata.AnnData`
`AnnData` object for validating trVAE (if None, trVAE will automatically split the data with
fraction of 80%/20%.
condition_encoder: dict
dictionary of encoded conditions (if None, trVAE will make one for data)
condition_key: str
name of conditions (domains) column in obs matrix
cell_type_key: str
name of cell_types (labels) column in obs matrix
n_epochs: int
number of epochs to iterate and optimize network weights
batch_size: int
number of samples to be used in each batch for network weights optimization
early_stop_limit: int
number of consecutive epochs in which network loss is not going lower.
After this limit, the network will stop training.
threshold: float
Threshold for difference between consecutive validation loss values
if the difference is upper than this `threshold`, this epoch will not
considered as an epoch in early stopping.
monitor: str
metric to be monitored for early stopping.
shuffle: boolean
if `True`, `train_adata` will be shuffled before training.
verbose: int
level of verbosity
save: boolean
if `True`, the model will be saved in the specified path after training.
# Returns
Nothing will be returned
# Example
```python
import scanpy as sc
import trvae
train_data = sc.read("train.h5ad")
valid_adata = sc.read("validation.h5ad")
n_conditions = len(train_adata.obs['condition'].unique().tolist())
network = trvae.archs.trVAEMulti(train_adata.shape[1], n_conditions)
network.train(train_adata, valid_adata, le=None,
condition_key="condition", cell_type_key="cell_label",
n_epochs=1000, batch_size=256
)
```
"""
train_labels_encoded, _ = label_encoder(train_adata, condition_encoder,
condition_key)
train_labels_onehot = to_categorical(train_labels_encoded,
num_classes=self.n_conditions)
callbacks = [
History(),
CSVLogger(filename="./csv_logger.log"),
]
if early_stop_limit > 0:
callbacks.append(
EarlyStopping(patience=early_stop_limit,
monitor=monitor,
min_delta=threshold))
if lr_reducer > 0:
callbacks.append(
ReduceLROnPlateau(monitor=monitor,
patience=lr_reducer,
verbose=verbose))
if verbose > 2:
callbacks.append(
LambdaCallback(on_epoch_end=lambda epoch, logs: print_message(
epoch, logs, n_epochs, verbose)))
fit_verbose = 0
else:
fit_verbose = verbose
if monitor_best:
os.makedirs(self.model_to_use, exist_ok=True)
callbacks.append(
ModelCheckpoint(filepath=os.path.join(self.model_to_use,
"best_model.h5"),
save_best_only=True,
monitor=monitor,
period=50))
if sparse.issparse(train_adata.X):
train_adata.X = train_adata.X.A
x = [train_adata.X, train_labels_onehot, train_labels_onehot]
y = [train_adata.X, train_labels_encoded]
if valid_adata is not None:
if sparse.issparse(valid_adata.X):
valid_adata.X = valid_adata.X.A
valid_labels_encoded, _ = label_encoder(valid_adata,
condition_encoder,
condition_key)
valid_labels_onehot = to_categorical(valid_labels_encoded,
num_classes=self.n_conditions)
x_valid = [valid_adata.X, valid_labels_onehot, valid_labels_onehot]
y_valid = [valid_adata.X, valid_labels_encoded]
history = self.cvae_model.fit(x=x,
y=y,
epochs=n_epochs,
batch_size=batch_size,
validation_data=(x_valid, y_valid),
shuffle=shuffle,
callbacks=callbacks,
verbose=fit_verbose)
else:
history = self.cvae_model.fit(x=x,
y=y,
epochs=n_epochs,
batch_size=batch_size,
validation_split=0.2,
shuffle=shuffle,
callbacks=callbacks,
verbose=fit_verbose)
if monitor_best:
self.restore_model()
elif save and not monitor_best:
self.save_model()
def train(self,
train_data,
use_validation=False,
valid_data=None,
n_epochs=25,
batch_size=32,
early_stop_limit=20,
threshold=0.0025,
initial_run=True,
shuffle=True):
"""
Trains the network `n_epochs` times with given `train_data`
and validates the model using validation_data if it was given
in the constructor function. This function is using `early stopping`
technique to prevent overfitting.
# Parameters
n_epochs: int
number of epochs to iterate and optimize network weights
early_stop_limit: int
number of consecutive epochs in which network loss is not going lower.
After this limit, the network will stop training.
threshold: float
Threshold for difference between consecutive validation loss values
if the difference is upper than this `threshold`, this epoch will not
considered as an epoch in early stopping.
full_training: bool
if `True`: Network will be trained with all batches of data in each epoch.
if `False`: Network will be trained with a random batch of data in each epoch.
initial_run: bool
if `True`: The network will initiate training and log some useful initial messages.
if `False`: Network will resume the training using `restore_model` function in order
to restore last model which has been trained with some training dataset.
# Returns
Nothing will be returned
# Example
```python
import scanpy as sc
import scgen
train_data = sc.read(train_katrain_kang.h5ad >>> validation_data = sc.read(valid_kang.h5ad)
network = scgen.CVAE(train_data=train_data, use_validation=True, validation_data=validation_data, model_path="./saved_models/", conditions={"ctrl": "control", "stim": "stimulated"})
network.train(n_epochs=20)
```
"""
if initial_run:
log.info("----Training----")
assign_step_zero = tensorflow.assign(self.global_step, 0)
_init_step = self.sess.run(assign_step_zero)
if not initial_run:
self.saver.restore(self.sess, self.model_to_use)
train_labels, le = label_encoder(train_data)
if use_validation and valid_data is None:
raise Exception("valid_data is None but use_validation is True.")
if use_validation:
valid_labels, _ = label_encoder(valid_data)
loss_hist = []
patience = early_stop_limit
min_delta = threshold
patience_cnt = 0
for it in range(n_epochs):
increment_global_step_op = tensorflow.assign(
self.global_step, self.global_step + 1)
_step = self.sess.run(increment_global_step_op)
current_step = self.sess.run(self.global_step)
train_loss = 0
for lower in range(0, train_data.shape[0], batch_size):
upper = min(lower + batch_size, train_data.shape[0])
if sparse.issparse(train_data.X):
x_mb = train_data[lower:upper, :].X.A
else:
x_mb = train_data[lower:upper, :].X
y_mb = train_labels[lower:upper]
_, current_loss_train = self.sess.run(
[self.solver, self.vae_loss],
feed_dict={
self.x: x_mb,
self.y: y_mb,
self.time_step: current_step,
self.size: len(x_mb),
self.is_training: True
})
train_loss += current_loss_train
print(
f"iteration {it}: {train_loss // (train_data.shape[0] // batch_size)}"
)
if use_validation:
valid_loss = 0
for lower in range(0, valid_data.shape[0], batch_size):
upper = min(lower + batch_size, valid_data.shape[0])
if sparse.issparse(valid_data.X):
x_mb = valid_data[lower:upper, :].X.A
else:
x_mb = valid_data[lower:upper, :].X
y_mb = valid_labels[lower:upper]
current_loss_valid = self.sess.run(self.vae_loss,
feed_dict={
self.x: x_mb,
self.y: y_mb,
self.time_step:
current_step,
self.size:
len(x_mb),
self.is_training:
False
})
valid_loss += current_loss_valid
loss_hist.append(valid_loss / valid_data.shape[0])
if it > 0 and loss_hist[it - 1] - loss_hist[it] > min_delta:
patience_cnt = 0
else:
patience_cnt += 1
if patience_cnt > patience:
os.makedirs(self.model_to_use, exist_ok=True)
save_path = self.saver.save(self.sess, self.model_to_use)
break
else:
os.makedirs(self.model_to_use, exist_ok=True)
save_path = self.saver.save(self.sess, self.model_to_use)
log.info(f"Model saved in file: {save_path}. Training finished")
def train(self,
train_adata,
valid_adata=None,
condition_encoder=None,
condition_key='condition',
n_epochs=25,
batch_size=32,
early_stop_limit=20,
lr_reducer=10,
threshold=0.0025,
monitor='val_loss',
shuffle=True,
verbose=2,
save=True):
"""
Trains the network `n_epochs` times with given `train_data`
and validates the model using validation_data if it was given
in the constructor function. This function is using `early stopping`
technique to prevent overfitting.
# Parameters
n_epochs: int
number of epochs to iterate and optimize network weights
early_stop_limit: int
number of consecutive epochs in which network loss is not going lower.
After this limit, the network will stop training.
threshold: float
Threshold for difference between consecutive validation loss values
if the difference is upper than this `threshold`, this epoch will not
considered as an epoch in early stopping.
full_training: bool
if `True`: Network will be trained with all batches of data in each epoch.
if `False`: Network will be trained with a random batch of data in each epoch.
initial_run: bool
if `True`: The network will initiate training and log some useful initial messages.
if `False`: Network will resume the training using `restore_model` function in order
to restore last model which has been trained with some training dataset.
# Returns
Nothing will be returned
# Example
```python
import scanpy as sc
import scgen
train_data = sc.read(train_katrain_kang.h5ad >>> validation_data = sc.read(valid_kang.h5ad)
network = scgen.CVAE(train_data=train_data, use_validation=True, validation_data=validation_data, model_path="./saved_models/", conditions={"ctrl": "control", "stim": "stimulated"})
network.train(n_epochs=20)
```
"""
train_adata = remove_sparsity(train_adata)
train_labels_encoded, self.condition_encoder = label_encoder(
train_adata, condition_encoder, condition_key)
train_labels_onehot = to_categorical(train_labels_encoded,
num_classes=self.n_conditions)
callbacks = [
History(),
CSVLogger(filename="./csv_logger.log"),
]
if early_stop_limit > 0:
callbacks.append(
EarlyStopping(patience=early_stop_limit,
monitor=monitor,
min_delta=threshold))
if lr_reducer > 0:
callbacks.append(
ReduceLROnPlateau(monitor=monitor,
patience=lr_reducer,
verbose=verbose))
if verbose > 2:
callbacks.append(
LambdaCallback(on_epoch_end=lambda epoch, logs: print_message(
epoch, logs, n_epochs, verbose)))
fit_verbose = 0
else:
fit_verbose = verbose
train_images = np.reshape(train_adata.X, (-1, *self.x_dim))
x = [train_images, train_labels_onehot, train_labels_onehot]
y = [train_images, train_labels_encoded]
if valid_adata is not None:
valid_adata = remove_sparsity(valid_adata)
valid_labels_encoded, _ = label_encoder(valid_adata,
condition_encoder,
condition_key)
valid_labels_onehot = to_categorical(valid_labels_encoded,
num_classes=self.n_conditions)
valid_images = np.reshape(valid_adata.X, (-1, *self.x_dim))
x_valid = [valid_images, valid_labels_onehot, valid_labels_onehot]
y_valid = [valid_images, valid_labels_encoded]
self.cvae_model.fit(x=x,
y=y,
epochs=n_epochs,
batch_size=batch_size,
validation_data=(x_valid, y_valid),
shuffle=shuffle,
callbacks=callbacks,
verbose=fit_verbose)
else:
self.cvae_model.fit(x=x,
y=y,
epochs=n_epochs,
batch_size=batch_size,
shuffle=shuffle,
callbacks=callbacks,
verbose=fit_verbose)
if save:
self.save_model()
