def make_mlp(xxx_todo_changeme, xxx_todo_changeme1, xxx_todo_changeme2,
xxx_todo_changeme3, args):
(X_train, y_train) = xxx_todo_changeme
(X_val, y_val) = xxx_todo_changeme1
(X_test, y_test) = xxx_todo_changeme2
(W, hb) = xxx_todo_changeme3
dense_params = {}
if W is not None and hb is not None:
dense_params['weights'] = (W, hb)
# define and initialize MLP model
mlp = Sequential([
Dense(args.n_hidden,
input_shape=(784, ),
kernel_regularizer=regularizers.l2(args.mlp_l2),
kernel_initializer=glorot_uniform(seed=1111),
**dense_params),
Activation('sigmoid'),
Dense(10, kernel_initializer=glorot_uniform(seed=2222)),
Activation('softmax'),
])
mlp.compile(optimizer=MultiAdam(lr=0.001,
lr_multipliers={
'dense_1': args.mlp_lrm[0],
'dense_2': args.mlp_lrm[1]
}),
loss='categorical_crossentropy',
metrics=['accuracy'])
# train and evaluate classifier
with Stopwatch(verbose=True) as s:
early_stopping = EarlyStopping(monitor=args.mlp_val_metric,
patience=12,
verbose=2)
reduce_lr = ReduceLROnPlateau(monitor=args.mlp_val_metric,
factor=0.2,
verbose=2,
patience=6,
min_lr=1e-5)
callbacks = [early_stopping, reduce_lr]
try:
mlp.fit(X_train,
one_hot(y_train, n_classes=10),
epochs=args.mlp_epochs,
batch_size=args.mlp_batch_size,
shuffle=False,
validation_data=(X_val, one_hot(y_val, n_classes=10)),
callbacks=callbacks)
except KeyboardInterrupt:
pass
y_pred = mlp.predict(X_test)
y_pred = unhot(one_hot_decision_function(y_pred), n_classes=10)
print("Test accuracy: {:.4f}".format(accuracy_score(y_test, y_pred)))
# save predictions, targets, and fine-tuned weights
np.save(args.mlp_save_prefix + 'y_pred.npy', y_pred)
np.save(args.mlp_save_prefix + 'y_test.npy', y_test)
W_finetuned, _ = mlp.layers[0].get_weights()
np.save(args.mlp_save_prefix + 'W_finetuned.npy', W_finetuned)
metrics=['accuracy'])
# train and evaluate classifier
with Stopwatch(verbose=True) as s:
early_stopping = EarlyStopping(monitor=args.mlp_val_metric,
patience=12,
verbose=2)
reduce_lr = ReduceLROnPlateau(monitor=args.mlp_val_metric,
factor=0.2,
verbose=2,
patience=6,
min_lr=1e-5)
callbacks = [early_stopping, reduce_lr]
try:
mlp.fit(X_train,
one_hot(y_train, n_classes=10),
epochs=args.mlp_epochs,
batch_size=args.mlp_batch_size,
shuffle=False,
validation_data=(X_val, one_hot(y_val, n_classes=10)),
callbacks=callbacks)
except KeyboardInterrupt:
pass
y_pred = mlp.predict(X_test)
y_pred = unhot(one_hot_decision_function(y_pred), n_classes=10)
print "Test accuracy: {:.4f}".format(accuracy_score(y_test, y_pred))
# save predictions, targets, and fine-tuned weights
np.save(args.mlp_save_prefix + 'y_pred.npy', y_pred)
np.save(args.mlp_save_prefix + 'y_test.npy', y_test)