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 load_elm(model_file):
elm = ELM(np.zeros((0, 0)), np.zeros((0, 0)), inputs_normalized=True)
elm.load(model_file)
return elm
