def main():
BATCH_SIZE = 64
EPOCHS = 3
# Load data
ds = dataset.load("mnist/fashion-mnist")
# transform into Tensorflow dataset
# max_text_len is an optional argument that fixes the maximum length of text labels
ds = ds.to_tensorflow(max_text_len=15)
# converting ds so that it can be directly used in model.fit
ds = ds.map(lambda x: to_model_fit(x))
# Splitting back into the original train and test sets
train_dataset = ds.take(60000)
test_dataset = ds.skip(60000)
train_dataset = train_dataset.batch(BATCH_SIZE)
test_dataset = test_dataset.batch(BATCH_SIZE)
model = create_CNN()
# model.summary()
model.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(train_dataset,
epochs=EPOCHS,
validation_data=test_dataset,
validation_steps=1)
def main():
BATCH_SIZE = 64
EPOCHS = 3
optimizer = Adam()
train_acc_metric = tf.keras.metrics.SparseCategoricalAccuracy()
test_acc_metric = tf.keras.metrics.SparseCategoricalAccuracy()
loss_fn = SparseCategoricalCrossentropy()
# Load data
ds = dataset.load("abhinavtuli/fashion-mnist")
# transform into Tensorflow dataset
ds = ds.to_tensorflow()
# Splitting back into the original train and test sets
train_dataset = ds.take(60000)
test_dataset = ds.skip(60000)
train_dataset = train_dataset.batch(BATCH_SIZE)
test_dataset = test_dataset.batch(BATCH_SIZE)
model = create_CNN()
# model.summary()
for epoch in range(EPOCHS):
print("\nStarting Training Epoch {}".format(epoch))
train(model, train_dataset, optimizer, loss_fn, train_acc_metric)
print("Training Epoch {} finished\n".format(epoch))
test(model, test_dataset, test_acc_metric)
def main():
EPOCHS = 3
BATCH_SIZE = 64
LEARNING_RATE = 0.01
MOMENTUM = 0.5
torch.backends.cudnn.enabled = False
random_seed = 2
torch.manual_seed(random_seed)
# Load data
ds = dataset.load("mnist/fashion-mnist")
# Transform into pytorch
# max_text_len is an optional argument that sets the maximum length of text labels, default is 30
ds = ds.to_pytorch(max_text_len=15)
# Splitting back into the original train and test sets, instead of random split
train_dataset = torch.utils.data.Subset(ds, range(60000))
test_dataset = torch.utils.data.Subset(ds, range(60000, 70000))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=BATCH_SIZE,
collate_fn=ds.collate_fn)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=BATCH_SIZE,
collate_fn=ds.collate_fn)
model = CNN()
optimizer = optim.SGD(model.parameters(),
lr=LEARNING_RATE,
momentum=MOMENTUM)
for epoch in range(EPOCHS):
print("Starting Training Epoch {}".format(epoch))
train(model, train_loader, optimizer)
print("Training Epoch {} finished\n".format(epoch))
test(model, test_loader)
# sanity check to see outputs of model
for batch in test_loader:
print("\nNamed Labels:", dataset.get_text(batch["named_labels"]))
print("\nLabels:", batch["labels"])
data = batch["data"]
data = torch.unsqueeze(data, 1)
output = model(data)
pred = output.data.max(1)[1]
print("\nPredictions:", pred)
break
def main():
BATCH_SIZE = 64
EPOCHS = 3
optimizer = Adam()
train_acc_metric = tf.keras.metrics.SparseCategoricalAccuracy()
test_acc_metric = tf.keras.metrics.SparseCategoricalAccuracy()
loss_fn = SparseCategoricalCrossentropy()
# Load data
ds = dataset.load("mnist/fashion-mnist")
# transform into Tensorflow dataset
# max_text_len is an optional argument that sets the maximum length of text labels, default is 30
ds = ds.to_tensorflow(max_text_len=15)
# Splitting back into the original train and test sets
train_dataset = ds.take(60000)
test_dataset = ds.skip(60000)
train_dataset = train_dataset.batch(BATCH_SIZE)
test_dataset = test_dataset.batch(BATCH_SIZE)
model = create_CNN()
# model.summary()
for epoch in range(EPOCHS):
print(f"\nStarting Training Epoch {epoch}")
train(model, train_dataset, optimizer, loss_fn, train_acc_metric)
print(f"Training Epoch {epoch} finished\n")
test(model, test_dataset, test_acc_metric)
# sanity check to see outputs of model
for batch in test_dataset:
print("\nNamed Labels:", dataset.get_text(batch["named_labels"]))
print("\nLabels:", batch["labels"])
output = model(tf.expand_dims(batch["data"], axis=3), training=False)
print(type(output))
pred = np.argmax(output, axis=-1)
print("\nPredictions:", pred)
break
def main():
EPOCHS = 3
BATCH_SIZE = 64
LEARNING_RATE = 0.01
MOMENTUM = 0.5
torch.backends.cudnn.enabled = False
random_seed = 2
torch.manual_seed(random_seed)
# Load data
ds = dataset.load("abhinavtuli/fashion-mnist")
# Transform into pytorch
ds = ds.to_pytorch()
# Splitting back into the original train and test sets, instead of random split
train_dataset = torch.utils.data.Subset(ds, range(60000))
test_dataset = torch.utils.data.Subset(ds, range(60000, 70000))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=BATCH_SIZE,
collate_fn=ds.collate_fn)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=BATCH_SIZE,
collate_fn=ds.collate_fn)
model = CNN()
optimizer = optim.SGD(model.parameters(),
lr=LEARNING_RATE,
momentum=MOMENTUM)
for epoch in range(EPOCHS):
print("Starting Training Epoch {}".format(epoch))
train(model, train_loader, optimizer)
print("Training Epoch {} finished\n".format(epoch))
test(model, test_loader)
from hub import dataset
# Load data
ds = dataset.load("arenbeglaryan/vocsegmentation")
ds = ds.to_tensorflow().batch(8)
# Iterate over the data
for batch in ds:
print(batch["data"], batch["labels"])
from hub import dataset
# Load data
ds = dataset.load("mnist/mnist")
# tansform into Tensorflow dataset
ds = ds.to_tensorflow().batch(8)
# Iterate over the data
for batch in ds:
print(batch["data"], batch["labels"])
import torch
from hub import dataset
# Load data
ds = dataset.load("abhinav/aerial-omdena")
# Transform into pytorch
ds = ds.to_pytorch()
ds = torch.utils.data.DataLoader(
ds, batch_size=2, collate_fn=ds.collate_fn
)
# Iterate over the data
for batch in ds:
print(batch["image_lat"])
print(batch["image_lon"])
print(batch["cluster_lat"])
print(batch["cluster_lon"])
print(batch["cons_pc"])
print(batch["nightlights"])
print(batch["nightlights_bin"])
print(batch["image"])
