def evaluate_elm(elm_file, validation_file, batch_size):
elm = ELM(np.zeros((0,0)), np.zeros((0,0)), inputs_normalized=True)
elm.load(elm_file)
data_file = h5py.File(validation_file, "r")
timer = time.time()
data = data_file['data']
labels = data_file['labels']
print("Data loaded", timeit(timer))
timer = time.time()
outer_batch_size = batch_size * 10 # How much can fit in memory at a time
num_batches = int(math.ceil(float(data.shape[0]) / outer_batch_size)) # float division, round up
skin_skin = 0
skin_but_not_skin = 0
not_skin_not_skin = 0
not_skin_but_skin = 0
for i in range(num_batches):
start = i * outer_batch_size
end = (i + 1) * outer_batch_size
predicted_y = elm.predict(data[start: end], batch_size=batch_size)
np.sign(predicted_y, out=predicted_y)
current_index = 0
label_batch = labels[start: end]
for i in range(len(label_batch)):
if label_batch[i] == 1 and predicted_y[current_index] == 1:
skin_skin += 1
elif label_batch[i] == -1 and predicted_y[current_index] == -1:
not_skin_not_skin += 1
elif label_batch[i] == -1 and predicted_y[current_index] == 1: # not_skin_but_skin
not_skin_but_skin += 1
elif label_batch[i] == 1 and predicted_y[current_index] == -1:
skin_but_not_skin += 1
# else:
# print(label_batch[i], predicted_y[current_index])
current_index += 1
print("Finished prediction and analysis", timeit(timer))
print("Neurons:")
for neuron_function, num_neurons, weight_matrix, bias_vector in elm.neurons:
print(neuron_function, num_neurons)
print(len(labels), "data points")
print("True-True:", skin_skin)
print("False-False:", not_skin_not_skin)
print("True-False:", skin_but_not_skin)
print("False-True:", not_skin_but_skin)
print("-" * 80)
def train_elm(filename, batch_size, neuron_allocation=None, inputs_normalized=False):
if not neuron_allocation:
neuron_allocation = {100: "sigm"}
data_file = h5py.File(filename, "r")
timer = time.time()
data = data_file['data']
labels = data_file['labels']
print("Data loaded", timeit(timer))
# accuracy_list = []
# for i in range(5):
timer = time.time()
elm = ELM(data, labels, inputs_normalized)
for key in neuron_allocation.keys():
elm.add_neurons(neuron_allocation[key], key)
elm.train()
print("ELM trained!", timeit(timer))
timer = time.time()
predicted_y = elm.predict(data, batch_size=batch_size)
print("ELM prediction finished", timeit(timer))
np.sign(predicted_y, out=predicted_y)
timer = time.time()
num_batches = math.ceil(float(labels.shape[0]) / batch_size) # float division, round up
current_index = 0
skin_skin = 0
skin_but_not_skin = 0
not_skin_not_skin = 0
not_skin_but_skin = 0
for label_batch in np.array_split(labels, num_batches):
for i in range(len(label_batch)):
if label_batch[i] == 1 and predicted_y[current_index] == 1:
skin_skin += 1
elif label_batch[i] == -1 and predicted_y[current_index] == -1:
not_skin_not_skin += 1
elif label_batch[i] == -1 and predicted_y[current_index] == 1: # not_skin_but_skin
not_skin_but_skin += 1
elif label_batch[i] == 1 and predicted_y[current_index] == -1:
skin_but_not_skin += 1
current_index += 1
accuracy = float(skin_skin + not_skin_not_skin) / (skin_skin + not_skin_not_skin + skin_but_not_skin + not_skin_but_skin)
print("Accuracy:", accuracy)
print("Finished analyzing errors", timeit(timer))
print(len(labels), "data points")
print("True-True:", skin_skin)
print("False-False:", not_skin_not_skin)
print("True-False:", skin_but_not_skin)
print("False-True:", not_skin_but_skin)
# print(sum(accuracy_list) / len(accuracy_list))
elm.save(data_file.filename[:data_file.filename.rindex('.')] + '.elm')
def load_elm(model_file):
elm = ELM(np.zeros((0, 0)), np.zeros((0, 0)), inputs_normalized=True)
elm.load(model_file)
return elm