batch_size = 50
learning_rate = 1e-3
niterations = 25000
momentum = 0.90
dropout = 0.00
gradient_clip = (-1.0, 1.0)
save_every = 1000
plot_every = 100
logs = {}
data = loader(batch_size=batch_size, permuted=permuted)
def dW(W):
load_weights(model, W)
forget(model)
inputs, labels = data.fetch()
preds = forward(model, inputs)
target = labels
backward(model, target)
gradients = extract_grads(model)
clipped_gradients = np.clip(gradients, gradient_clip[0], gradient_clip[1])
loss = -1.0 * np.sum(labels * np.log(preds)) / batch_size
batch_size = 50
learning_rate = 1e-2
niterations = 25000
momentum = 0.9
dropout = 0.10
gradient_clip = (-1.0, 1.0)
save_every = 1000
plot_every = 100
logs = {}
data = loader(batch_size=batch_size)
def dW(W):
load_weights(model, W)
forget(model)
inputs, labels = data.fetch()
preds = forward(model, inputs)
target = labels
backward(model, target)
gradients = extract_grads(model)
clipped_gradients = np.clip(gradients, gradient_clip[0], gradient_clip[1])
loss = -1.0 * np.sum(labels * np.log(preds)) / batch_size
batch_size = 16
learning_rate = 1e-3
niterations = 25000
momentum = 0.90
dropout = 0.00
gradient_clip = (-1.0, 1.0)
save_every = 1000
plot_every = 100
logs = {}
data = loader(batch_size=batch_size, permuted=permuted)
def dW(W):
load_weights(model, W)
forget(model)
inputs, labels = data.fetch()
preds = forward(model, inputs)
target = labels
backward(model, target)
gradients = extract_grads(model)
clipped_gradients = np.clip(gradients, gradient_clip[0], gradient_clip[1])
loss = -1.0 * np.sum(labels * np.log(preds)) / batch_size
gradient_norm = (gradients ** 2).sum() / gradients.size
batch_size = 50
learning_rate = 1e-2
niterations = 25000
momentum = 0.9
dropout = 0.10
gradient_clip = (-1.0, 1.0)
save_every = 1000
plot_every = 100
logs = {}
data = loader(batch_size=batch_size)
def dW(W):
load_weights(model, W)
forget(model)
inputs, labels = data.fetch()
preds = forward(model, inputs)
target = labels
backward(model, target)
gradients = extract_grads(model)
clipped_gradients = np.clip(gradients, gradient_clip[0], gradient_clip[1])
loss = -1.0 * np.sum(labels * np.log(preds)) / batch_size
gradient_norm = (gradients ** 2).sum() / gradients.size
