def mc_mlp_val(x_train,
y_train,
size_input,
hidden_layers,
nodes_hidden_layers,
size_output=2,
runs=1000,
n_samples=5):
max_acc = 0.0
max_conf = []
es = EarlyStopping(monitor='val_loss',
mode='min',
verbose=0,
patience=0,
restore_best_weights=True)
mlp_cache = {}
for _ in tqdm(range(runs)):
# generate random mlp
conf = gen_random_mlp(size_input, hidden_layers, nodes_hidden_layers,
size_output)
if conf not in mlp_cache:
acc = 0.0
for _ in range(n_samples):
model = MLP(conf, use_bias_input=False)
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(x_train,
y_train,
epochs=50,
batch_size=300,
validation_split=0.2,
callbacks=[es],
verbose=0)
acc += history.history['val_accuracy'][-1]
acc /= n_samples
mlp_cache[conf] = acc
if acc > max_acc:
max_acc = acc
max_conf = conf
return max_conf, max_acc