adamoudad

model.train()
for epoch in range(1, 5):
    with tqdm(train_loader, unit="batch") as tepoch:
        for data, target in tepoch:
            tepoch.set_description(f"Epoch {epoch}")
            
            data, target = data.to(device), target.to(device)
            optimizer.zero_grad()
            output = model(data)
            predictions = output.argmax(dim=1, keepdim=True).squeeze()

adamoudad

def myfunc(a: int = 1, b: str = "", c: list = []):
    pass

adamoudad

def myfunc(a, b, c):
    pass

config = {
    "a": 1,
    "b": 2,
    "c": 5
}

myfunc(**config)

adamoudad

def myfunc(a, b, c, d, e, f):
    # Do stuff
    pass

myfunc(a=1, b="foo", c=42, d=100, f="bar")

adamoudad

class EmptyClass:
    pass

def some_function():
    pass

adamoudad

for i in range(n):
    # Do stuff...
    if condition_is_met:
        break
else:
    # This will be executed only if break is never read.

adamoudad

for i in range(n):
    # Do stuff...
    if condition_is_met:
        break

adamoudad

for i in range(n):
    try:
        data = next(dataiter)
    except ValueError:
        continue
    # The code here is ignored if ValueError is raised in the "try" block
    # Otherwise, run the rest of the loop here ...

adamoudad

from tensorflow.keras.layers import Bidirectional, LSTM, Dense, Embedding
from tensorflow.keras.models import Sequential
model = Sequential([
    Embedding(input_dim, embedding_dim),
    Bidirectional(LSTM(hidden_dim, return_sequences=True)),
    Bidirectional(LSTM(hidden_dim)),
    Dense(1, activation="sigmoid")
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=["accuracy"])
model.summary()

adamoudad

epoch_history["loss"].append(sum(batch_history["loss"]) / len(batch_history["loss"]))
epoch_history["accuracy"].append(sum(batch_history["accuracy"]) / len(batch_history["accuracy"]))
model.eval()
print("Validation...")
val_loss, val_accuracy = validate(model, valid_loader)
epoch_history["val_loss"].append(val_loss)
epoch_history["val_accuracy"].append(val_accuracy)
print(f"{epoch_history=}")