def testModel():
# Create models
print("Creating Autoencoder, Encoder and Generator...")
autoencoder, encoder, decoder = getModels()
# Load Autoencoder weights
print("Loading weights...")
autoencoder.load_weights(modelsPath + modelName)
# Load dataset to test
print("Loading dataset...")
X_train, X_test = loadDataset()
# Visualization functions
visualizeReconstructedImages(X_train[:16],
X_test[:16],
autoencoder,
save=True)
def trainModel(startEpoch=0):
# Create models
print("Creating Autoencoder...")
autoencoder, _, _ = getModels()
autoencoder.compile(optimizer=RMSprop(lr=0.00025), loss="mse")
# From which we start
if startEpoch > 0:
# Load Autoencoder weights
print("Loading weights...")
autoencoder.load_weights(modelsPath + modelName)
print("Loading dataset...")
X_train, X_test = loadDataset()
# Compute number of batches
nbBatch = int(X_train.shape[0] / batchSize)
# Train the Autoencoder on dataset
print(
"Training Autoencoder for {} epochs with {} batches per epoch and {} samples per batch."
.format(nbEpoch, nbBatch, batchSize))
print("Run id: {}".format(runID))
# Debug utils writer
writer = tf.summary.FileWriter("/tmp/logs/" + runID)
batchTimes = [0. for i in range(5)]
# For each epoch
for epoch in range(startEpoch, nbEpoch):
# For each batch
for batchIndex in range(nbBatch):
batchStartTime = time.time()
# Get batch
X = X_train[batchIndex * batchSize:(batchIndex + 1) * batchSize]
# Train on batch
autoencoderLoss = autoencoder.train_on_batch(X, X)
trainingSummary = tf.Summary.Value(
tag="Loss", simple_value=float(autoencoderLoss))
# Compute ETA
batchTime = time.time() - batchStartTime
batchTimes = batchTimes[1:] + [batchTime]
eta = getETA(
sum(batchTimes) / len(batchTimes), nbBatch, batchIndex,
nbEpoch, epoch)
# Save reconstructions on train/test samples
if batchIndex % 2 == 0:
visualizeReconstructedImages(X_train[:16],
X_test[:16],
autoencoder,
save=True,
label="{}_{}".format(
epoch, batchIndex))
# Validation & Tensorboard Debug
if batchIndex % 20 == 0:
validationLoss = autoencoder.evaluate(X_test[:512],
X_test[:512],
batch_size=256,
verbose=0)
validationSummary = tf.Summary.Value(
tag="Validation Loss", simple_value=float(validationLoss))
summary = tf.Summary(
value=[trainingSummary, validationSummary])
print(
"Epoch {}/{} - Batch {}/{} - Loss: {:.3f}/{:.3f} - ETA:".
format(epoch + 1, nbEpoch, batchIndex + 1, nbBatch,
autoencoderLoss, validationLoss), eta)
else:
print(
"Epoch {}/{} - Batch {}/{} - Loss: {:.3f} - ETA:".format(
epoch + 1, nbEpoch, batchIndex + 1, nbBatch,
autoencoderLoss), eta)
summary = tf.Summary(value=[
trainingSummary,
])
writer.add_summary(summary, epoch * nbBatch + batchIndex)
#Save model every epoch
print("Saving autoencoder...")
autoencoder.save_weights(modelsPath + modelName, overwrite=True)