params=W,
learning_rate=LR)
updates = quantized_net.clipping_scaling(updates, mlp)
# other parameters updates
params = lasagne.layers.get_all_params(mlp,
trainable=True,
quantized=False)
updates = OrderedDict(updates.items() + lasagne.updates.adam(
loss_or_grads=loss, params=params, learning_rate=LR).items())
test_output = lasagne.layers.get_output(mlp, deterministic=True)
test_loss = T.mean(T.sqr(T.maximum(0., 1. - target * test_output)))
test_err = T.mean(T.neq(T.argmax(test_output, axis=1),
T.argmax(target, axis=1)),
dtype=theano.config.floatX)
# Compile a function performing a training step on a mini-batch (by giving the updates dictionary)
# and returning the corresponding training loss:
train_fn = theano.function([input, target, LR], loss, updates=updates)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input, target], [test_loss, test_err])
print('Training...')
quantized_net.train(train_fn, val_fn, mlp, batch_size, LR_start, LR_decay,
num_epochs, train_set.X, train_set.y, valid_set.X,
valid_set.y, test_set.X, test_set.y, save_path,
shuffle_parts)
updates = lasagne.updates.adam(loss_or_grads=W_grads,
params=W,
learning_rate=LR)
updates = quantized_net.clipping_scaling(updates, mlp)
# other parameters updates
params = lasagne.layers.get_all_params(mlp,
trainable=True,
quantized=False)
updates = OrderedDict(updates.items() + lasagne.updates.adam(
loss_or_grads=loss, params=params, learning_rate=LR).items())
test_output = lasagne.layers.get_output(mlp, deterministic=True)
test_loss = T.mean(T.sqr(T.maximum(0., 1. - target * test_output)))
test_err = T.mean(T.neq(T.argmax(test_output, axis=1),
T.argmax(target, axis=1)),
dtype=theano.config.floatX)
# Compile a function performing a training step on a mini-batch (by giving the updates dictionary)
# and returning the corresponding training loss:
train_fn = theano.function([input, target, LR], loss, updates=updates)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input, target], [test_loss, test_err])
print('Training...')
quantized_net.train(train_fn, val_fn, mlp, batch_size, LR_start, LR_decay,
num_epochs, train_images, train_labels, valid_images,
valid_labels, test_images, test_labels, save_path,
shuffle_parts)
test_loss = T.mean(T.sqr(T.maximum(0., 1. - target * test_output)))
test_err = T.mean(T.neq(T.argmax(test_output, axis=1),
T.argmax(target, axis=1)),
dtype=theano.config.floatX)
# Compile a function performing a training step on a mini-batch (by giving the updates dictionary)
# and returning the corresponding training loss:
train_fn = theano.function([input, target, LR], loss, updates=updates)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input, target], [test_loss, test_err])
print('Training...')
my_val_loss, my_train_loss, my_val_err, mlp_best = quantized_net.train(
train_fn, val_fn, mlp, batch_size, LR_start, LR_decay, num_epochs,
X_train, y_train, X_val, y_val, X_test, y_test, save_path,
shuffle_parts)
output = lasagne.layers.get_output(mlp_best, X_test)
print(output.shape.eval())
print((output.eval()).shape)
makeRoc(X_test, labels, y_test, output.eval(), "./")
plt.figure()
plt.plot(my_val_loss, label='validation')
plt.plot(my_train_loss, label='train')
plt.legend()
plt.xlabel('epoch')
plt.ylabel('loss')
plt.savefig("./loss.png")
test_loss = T.mean(T.sqr(T.maximum(0., 1. - target * test_output)))
test_err = T.mean(T.neq(T.argmax(test_output, axis=1),
T.argmax(target, axis=1)),
dtype=theano.config.floatX)
# Compile a function performing a training step on a mini-batch (by giving the updates dictionary)
# and returning the corresponding training loss:
train_fn = theano.function([input, target, LR], loss, updates=updates)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input, target], [test_loss, test_err])
print('Training...')
quantized_net.train(train_fn,
val_fn,
cnn,
batch_size,
LR_start,
LR_decay,
num_epochs,
train_set.X,
train_set.y,
valid_set.X,
valid_set.y,
test_set.X,
test_set.y,
save_path=save_path,
shuffle_parts=shuffle_parts,
rotations=random_rot_range)
