def main(args):
batch_size = 128
ds_train, ds_test, info = load_data(batch_size)
image_shape = info.features["image"].shape
image_size = tf.reduce_prod(image_shape)
num_train_samples = info.splits["train"].num_examples
num_test_samples = info.splits["test"].num_examples
cnn = Cnn()
if "init_model" in args.cnn_mode:
cnn.create_model(batch_size=batch_size,
image_shape=image_shape,
feature_outputs=int(100))
if "train" in args.cnn_mode:
cnn.train(ds_train,
ds_test,
epochs=10,
steps_per_epoch=int(num_train_samples / batch_size))
if "save" in args.cnn_mode:
cnn.save()
if "load" in args.cnn_mode:
cnn.load_combined_model()
if "test" in args.cnn_mode:
cnn.test(ds_test)
gp = DeepKernelGP(ds_train,
num_train_samples,
image_size,
10,
cnn.feature_extractor,
num_inducing_points=100)
if "train" in args.gp_mode:
gp.train(10000)
if "test" in args.gp_mode:
gp.test(ds_test, image_size, batch_size, num_test_samples)
def main():
epochs = 100
#x, y = load_data(DATA_PATH, verbose=False, num_samples=5)
x = np.load('/global/scratch/alex_vlissidis/X.npz')['x']
y = np.load('/global/scratch/alex_vlissidis/y.npz')['y']
print("training data shapes:", x.shape, y.shape)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
print("building model...")
print(x.shape)
print(y.shape[1])
model = Cnn(x.shape[1:], y.shape[1])
model.build()
history = model.train(x_train, y_train, epochs=epochs)
print("Saving model...")
model.model.save('models/model.h5')
print("Plotting...")
f, (ax1, ax2) = plt.subplots(2, 1)
ax1.plot(range(1, epochs + 1),
history.history['val_acc'],
'tab:blue',
label="validation accuracy")
ax1.plot(range(1, epochs + 1),
history.history['acc'],
'tab:red',
label="training accuracy")
ax2.plot(range(1, epochs + 1),
history.history['loss'],
'tab:orange',
label="loss")
ax2.plot(range(1, epochs + 1),
history.history['val_loss'],
'tab:green',
label="validation loss")
ax1.legend()
ax2.legend()
f.savefig('figures/training.png', dpi=300)
print("Done.")
testingAccuracy = []
testingLoss = []
epochs = list(range(0, cfg.epochs))
#training
for epoch in range(0, cfg.epochs):
#records weights to see if they change after training
weights1 = []
for param in cnn.parameters():
weights1.append(param.clone())
#training
print("Training Epoch", str(epoch + 1), "of", str(cfg.epochs) + ":")
trainAcc, trainLoss = cnn.train(trainImgs, trainOneHotVecs,
cfg.trainBatchSize)
trainingAccuracy.append(trainAcc)
trainingLoss.append(trainLoss)
#prints if weights are unchanged
weights2 = []
unchangedWeights = 0
for param in cnn.parameters():
weights2.append(param.clone())
for i in zip(weights1, weights2):
if torch.equal(i[0], i[1]):
unchangedWeights += 1
if unchangedWeights > 0:
print(unchangedWeights, "of", len(weights1), "Weights Unchanged")