def fit(self, X, Y):
"""
X: of shape [data-size, feature-size]
Y: of shape [data-size]
"""
self.feature_size = X.shape[-1]
# w of shape [feature-size]
self.w = np.random.rand(self.feature_size)
# b of shape [1]
self.b = np.random.rand(1)
for step in range(self.max_steps):
# pred of shape [data-size]
pred = self._predict(X)
# Bias gradient of shape [data-size]
gradient_b = Y - pred
# Weight gradient of shape [data-size, feature-size]
gradient_w = gradient_b[:, None] * X
# get mean of gradient across all data
gradient_b = gradient_b.mean(axis=0)
gradient_w = gradient_w.mean(axis=0)
self.w += gradient_w
self.b += gradient_b
loss = binary_cross_entropy(pred, Y)
if self.verbose:
print(f"Step {step}, Loss is {loss}...")
def calc_obj_loss(self, true_obj, pred_obj, ignore_mask):
obj_entropy = binary_cross_entropy(pred_obj, true_obj)
obj_loss = true_obj * obj_entropy
noobj_loss = (1 - true_obj) * obj_entropy * ignore_mask
obj_loss = tf.reduce_sum(obj_loss, axis=(1, 2, 3, 4))
noobj_loss = tf.reduce_sum(noobj_loss,
axis=(1, 2, 3, 4)) * self.lamda_noobj
return obj_loss + noobj_loss
def calc_class_loss(self, true_obj, true_class, pred_class):
"""
calculate loss of class prediction
inputs:
true_obj: if the object present from ground truth in shape of (batch, grid, grid, anchor, 1)
true_class: one-hot class from ground truth in shape of (batch, grid, grid, anchor, num_classes)
pred_class: one-hot class from model prediction in shape of (batch, grid, grid, anchor, num_classes)
outputs:
class_loss: class loss
"""
# Yolov1:
# "Note that the loss function only penalizes classification error
# if an object is present in that grid cell (hence the conditional
# class probability discussed earlier).
class_loss = binary_cross_entropy(pred_class, true_class)
class_loss = true_obj * class_loss
class_loss = tf.reduce_sum(class_loss, axis=(1, 2, 3, 4))
return class_loss
def calc_obj_loss(self, true_obj, pred_obj, ignore_mask):
"""
calculate loss of objectness: sum of L2 distances
inputs:
true_obj: objectness from ground truth in shape of (batch, grid, grid, anchor, num_classes)
pred_obj: objectness from model prediction in shape of (batch, grid, grid, anchor, num_classes)
outputs:
obj_loss: objectness loss
"""
obj_entropy = binary_cross_entropy(pred_obj, true_obj)
obj_loss = true_obj * obj_entropy
noobj_loss = (1 - true_obj) * obj_entropy * ignore_mask
obj_loss = tf.reduce_sum(obj_loss, axis=(1, 2, 3, 4))
noobj_loss = tf.reduce_sum(noobj_loss,
axis=(1, 2, 3, 4)) * self.lamda_noobj
return obj_loss + noobj_loss
def calc_class_loss(self, true_obj, true_class, pred_class):
class_loss = binary_cross_entropy(pred_class, true_class)
class_loss = true_obj * class_loss
class_loss = tf.reduce_sum(class_loss, axis=(1, 2, 3, 4))
return class_loss
H = tf.layers.max_pooling2d(H, pool_size=(2, 2), strides=(1, 1), padding='SAME', name=str(layer_i))
"""
# Number of filters and layers of the FCN
layers = [100, 100, 4]
for layer_i, n_output_i in enumerate(layers):
H, W = fully_connected(H, n_output=n_output_i, name=layer_i)
if layer_i == len(layers) - 1:
H = tf.nn.softmax(H)
else:
H = tf.nn.relu(H)
Y_predicted = H
# Cost function
loss = binary_cross_entropy(Y_predicted, Y)
cost = tf.reduce_mean(tf.reduce_sum(loss, 1))
# Measure of accuracy
predicted_y = tf.argmax(Y_predicted, 1)
actual_y = tf.argmax(Y, 1)
correct_prediction = tf.equal(predicted_y, actual_y)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
# Training parameters
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
n_epochs = 200
sess = tf.Session()
sess.run(tf.global_variables_initializer())
normalize,
load_model,
binary_cross_entropy,
roc,
)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="")
parser.add_argument("dataset_test",
type=open_datafile,
help="dataset to use")
parser.add_argument("model", help="model to use")
parser.add_argument("-vi",
"--visu",
help="Display graphs",
action="store_true")
args = parser.parse_args()
n = load_model(args.model)
# test = args.dataset_test.drop(args.dataset_test.columns[0], axis=1)
test = args.dataset_test[[
1, 2, 3, 8, 11, 12, 17, 18, 19, 21, 26, 28, 30, 31
]]
test = normalize(test)
test = np.array(test)
error, acc = binary_cross_entropy(test, n)
print(f"Cross Binary Entropy Error = {error:.5f}")
print(f"Accuracy = {acc:.5f}")
if args.visu is True:
roc(test, n)