def mean_accuracy_metric(y_true, y_pred):
sum_of_accuracy = categorical_accuracy(y_true[0:lenght_of_input],
y_pred[0:lenght_of_input])
for i in range(1, number_of_inputs):
start_index = i * lenght_of_input
end_index = (i + 1) * lenght_of_input
accuracy = categorical_accuracy(y_true[start_index:end_index],
y_pred[start_index:end_index])
sum_of_accuracy = sum_of_accuracy[-1] + accuracy[-1]
return sum_of_accuracy
def market_attribute_accuracy(y_true, y_pred):
# binary categories
acc = binary_accuracy(y_true[:, binary_1], y_pred[:, binary_1]) * 0.5
acc += binary_accuracy(y_true[:, binary_2], y_pred[:, binary_2]) * 0.5
# top colors
acc_top_color = categorical_accuracy(y_true[:, up_colors],
y_pred[:, up_colors])
# down colors
acc_down_color = categorical_accuracy(y_true[:, down_colors],
y_pred[:, down_colors])
# print(acc, acc_down_color, acc_top_color)
return acc * 9 / 11 + acc_down_color * 1 / 11 + acc_top_color * 1 / 11
def __init__(self, Nin, Nh_l,
Nout): # Nin은 입력 크기, Nh_I는 은닉층 크기, Nout은 출력층 크기
self.X_ph = tf.placeholder(tf.float32, shape=(None, Nin)) # 입력 플레이스홀더
self.L_ph = tf.placeholder(tf.float32,
shape=(None, Nout)) # 레이블 플레이스홀더
# Modeling
H = Dense(Nh_l[0], activation='relu')(
self.X_ph) # 입력 플레이스홀더를 넣고, 첫 번째 은닉층 크기만큼 반환하는 은닉층1
H = Dropout(0.5)(H) # 드롭아웃
H = Dense(Nh_l[1], activation='relu')(
H) # 입력 플레이스홀더를 넣고, 두 번째 은닉층 크기만큼 반환하는 은닉층1
H = Dropout(0.25)(H) # 드롭아웃
self.Y_tf = Dense(Nout, activation='softmax')(H) # 출력층, 소프트맥스
# Operation
self.Loss_tf = tf.reduce_mean(
categorical_crossentropy(self.L_ph,
self.Y_tf)) # 손실함수는 레이블과 출력 간 크로스엔트로피
self.Train_tf = tf.train.AdamOptimizer().minimize(
self.Loss_tf) # 최적화함수는 에이담
self.Acc_tf = categorical_accuracy(self.L_ph,
self.Y_tf) # 정확도 산출은 케라스 함수로
self.Init_tf = tf.global_variables_initializer(
) # 초기화 함수는 텐서플로 글로벌 전역 초기화 함수
def kaggle_sliced_accuracy(y_true, y_pred, slice_weights=[1.] * 11):
question_slices = [
slice(0, 3),
slice(3, 5),
slice(5, 7),
slice(7, 9),
slice(9, 13),
slice(13, 15),
slice(15, 18),
slice(18, 25),
slice(25, 28),
slice(28, 31),
slice(31, 37)
]
accuracy_slices = [
categorical_accuracy(y_true[:, question_slices[i]],
y_pred[:, question_slices[i]]) * slice_weights[i]
for i in range(len(question_slices))
]
accuracy_slices = T.cast(accuracy_slices, 'float32')
return {
'sliced_accuracy_mean': T.mean(accuracy_slices),
'sliced_accuracy_std': T.std(accuracy_slices)
}
def fnc_score(y_true, y_pred):
"Assumes two outputs: related and stance"
y_true_related, y_true_stance = tf.split(y_true, 2, axis=1)
y_pred_related, y_pred_stance = tf.split(y_pred, 2, axis=1)
print(y_true_related, y_true_stance, y_pred_related, y_pred_stance)
a1 = categorical_accuracy(y_true_related, y_pred_related)
a2 = categorical_accuracy(y_true_stance, y_pred_stance)
print(a1, a2)
return (
tf.multiply(a1, 0.25) +
tf.multiply(a2, 0.75)
#tf.multiply(categorical_accuracy(y_true_related, y_pred_related), 0.25) +
#tf.multiply(categorical_accuracy(y_true_stance, y_pred_stance), 0.75)
)
def _generic_accuracy(y_true, y_pred):
if K.int_shape(y_pred)[1] == 1:
return binary_accuracy(y_true, y_pred)
if K.int_shape(y_true)[-1] == 1:
return sparse_categorical_accuracy(y_true, y_pred)
return categorical_accuracy(y_true, y_pred)
def ctm_acc1(y_true, y_pred):
# print("ctm_acc1")
# true = tf.split(y_true, 3, axis=-1)[0]
pred_list = tf.split(y_pred, 3, axis=-1)
pred = pred_list[0] + pred_list[1] + 0.1 * pred_list[2]
# print(categorical_accuracy(true, pred))
return categorical_accuracy(y_true, pred)
def acc_kldiv(y_in,x):
"""
Corrected accuracy to be used with custom loss_kldiv
"""
h = y_in[:,0:NBINS]
y = y_in[:,NBINS:]
return categorical_accuracy(y, x)
def certain_predictions_acc(y_true, y_pred):
"""
accuracy of predictions that are certain
"""
c_pred_ind = _certain_ind(y_pred, uncertain=False)
return categorical_accuracy(K.gather(y_true, c_pred_ind),
K.gather(y_pred, c_pred_ind))
def trip_accuracy(y_true, y_pred):
# Seperate embeddings into the triplets
num_classes = y_pred._keras_shape[-1]
trip_pred = K.reshape(y_pred, (-1, 3, num_classes))
trip_labels = K.reshape(y_true, (-1, 3, num_classes))
# Return correct classification
return metrics.categorical_accuracy(trip_labels[:, 0], trip_pred[:, 0])
def L_acc(y_true, y_pred):
# Confidence component (aka objectness)
y_true_confidence = y_true[:, 0]
y_pred_confidence = K.round(y_pred[:, 0])
#confidence_accuracy = K.cast(K.equal(y_true_confidence, K.round(y_pred_confidence)), K.floatx())
# Class component
y_true_classes = y_true[:, 4:]
y_pred_classes = y_pred[:, 4:]
classes_accuracy = categorical_accuracy(y_true_classes, y_pred_classes)
#y_true_coord = y_true[:, 1:4]
#y_pred_coord = y_pred[:, 1:4]
true_xy = y_true[:, 1:3]
pred_xy = y_pred[:, 1:3]
true_r = y_true[:, 3:4]
pred_r = y_pred[:, 3:4]
# compute IOU, using the top,left,bottom,right representation.
true_mins = true_xy - true_r
true_maxes = true_xy + true_r
pred_mins = pred_xy - pred_r
pred_maxes = pred_xy + pred_r
intersect_mins = K.maximum(pred_mins, true_mins)
intersect_maxes = K.minimum(pred_maxes, true_maxes)
intersect_wh = K.maximum(intersect_maxes - intersect_mins, 0.)
intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]
pred_areas = 4. * pred_r[..., 0] * pred_r[..., 0] # a square
true_areas = 4. * true_r[..., 0] * true_r[..., 0]
union_areas = pred_areas + true_areas - intersect_areas
iou_scores = intersect_areas / union_areas
iou_accuracy = K.cast(iou_scores > 0.6, K.floatx())
#joint_accuracy = confidence_accuracy * classes_accuracy * iou_accuracy
joint_accuracy = (y_true_confidence * y_pred_confidence * classes_accuracy * iou_accuracy) + \
((1.0 - y_true_confidence) * (1.0 - y_pred_confidence)) # if background, dont care about class or iou
'''
joint_accuracy = tf.Print(joint_accuracy, [K.dtype(confidence_accuracy),
K.dtype(classes_accuracy),
K.dtype(iou_accuracy),
K.dtype(joint_accuracy)
], message='confidence, class, iou, joint dtype: ', summarize=20)
'''
return joint_accuracy
def adv_acc(y, _):
# Generate adversarial examples
x_adv = fgsm(model, y, eps=eps, clip_min=clip_min, clip_max=clip_max)
# Consider the attack to be constant
x_adv = K.stop_gradient(x_adv)
# Accuracy on the adversarial examples
preds_age, preds_race, preds_gender = model(x_adv)
return categorical_accuracy(y, preds_race)
def test_sparse_categorical_accuracy_correctness():
y_a = K.variable(np.random.randint(0, 7, (6,)), dtype=K.floatx())
y_b = K.variable(np.random.random((6, 7)), dtype=K.floatx())
# use one_hot embedding to convert sparse labels to equivalent dense labels
y_a_dense_labels = K.cast(K.one_hot(K.cast(y_a, dtype='int32'), num_classes=7),
dtype=K.floatx())
sparse_categorical_acc = metrics.sparse_categorical_accuracy(y_a, y_b)
categorical_acc = metrics.categorical_accuracy(y_a_dense_labels, y_b)
assert np.allclose(K.eval(sparse_categorical_acc), K.eval(categorical_acc))
def test_sparse_categorical_accuracy_correctness():
y_a = K.variable(np.random.randint(0, 7, (6,)), dtype=K.floatx())
y_b = K.variable(np.random.random((6, 7)), dtype=K.floatx())
# use one_hot embedding to convert sparse labels to equivalent dense labels
y_a_dense_labels = K.cast(K.one_hot(K.cast(y_a, dtype='int32'), num_classes=7),
dtype=K.floatx())
sparse_categorical_acc = metrics.sparse_categorical_accuracy(y_a, y_b)
categorical_acc = metrics.categorical_accuracy(y_a_dense_labels, y_b)
assert np.allclose(K.eval(sparse_categorical_acc), K.eval(categorical_acc))
def true_accuracy(y_true, y_pred):
'''
Ignore START_OF_SENTENCE and END_OF_SENTENCE when calculating accuracy.
Also ignore zero paddings.
'''
# ignore SOS, EOS
trimmed_y_true = y_true[:, 1:-1, :]
trimmed_y_pred = y_pred[:, 1:-1, :]
return categorical_accuracy(trimmed_y_true, trimmed_y_pred)
def adv_acc(y, _):
# Generate adversarial examples
y_gender = tf.get_default_graph().get_tensor_by_name("gender_target:0")
x_adv = fgsm(model, y_gender, eps=eps, clip_min=clip_min, clip_max=clip_max)
# Consider the attack to be constant
x_adv = K.stop_gradient(x_adv)
# Accuracy on the adversarial examples
_, preds_age = model(x_adv)
return categorical_accuracy(y, preds_age)
def acc_reg(y_in,x_in):
"""
Corrected accuracy to be used with custom loss_reg
"""
h = y_in[:,0:NBINS]
y = y_in[:,NBINS:]
hpred = x_in[:,0:NBINS]
ypred = x_in[:,NBINS:]
return categorical_accuracy(y, ypred)
def accuracy2(args):
y_pred, y_true = args
y_pred = K.softmax(y_pred, axis=-1)
match_board = []
for i in range(3):
tmp = tf.ones_like(y_true) * i
tmp = tf.cast(tf.equal(y_true, tmp), tf.float32)
match_board.append(tmp)
y_true_onehot = tf.concat(match_board, axis=1)
acc = categorical_accuracy(y_true_onehot, y_pred)
return acc
def evaluate(conf):
_, x_test, _, _, y_test, _, _, _, _, x_test_units, y_test_units = full_data_pipeline(
conf)
model = build_model(conf)
tu_rolls, tu_features = uncombine_features(x_test_units)
tu_x = []
tu_x.append(tu_rolls)
tu_x.extend(tu_features)
mod_tu_x = []
for feat in tu_x:
mod_tu_x.append(np.stack(feat))
tu_y = np.stack(y_test_units)
print('Single sample test results : ')
evaluate_model(conf, model, mod_tu_x, tu_y)
print('Full track test results : ')
pianos_s, feats_s = uncombine_features(x_test)
y_pred = []
for s in range(len(pianos_s)):
piano_s = []
piano_s.append(pianos_s[s])
feat_s = []
for ft in feats_s:
feat_s.append(ft[s])
x = prediction_data(conf, piano_s, feat_s)
mod_x = []
for feat in x:
mod_x.append(np.stack(feat))
y = predict_model(conf, model, mod_x)
y = np.argmax(y, axis=1)
votes = np.bincount(y)
y_pred_s = np.zeros((conf['dataset']['num_class']))
for i in range(y_pred_s.shape[0]):
if (i < len(votes)):
y_pred_s[i] = votes[i]
else:
y_pred_s[i] = 0
y_pred_s = y_pred_s / np.sum(y_pred_s)
y_pred.append(y_pred_s)
y_test = np.stack(y_test)
y_pred = np.stack(y_pred)
y_test = y_test.astype('float32')
y_pred = y_pred.astype('float32')
y_true = K.constant(y_test)
y_pred = K.constant(y_pred)
loss = K.categorical_crossentropy(target=y_true, output=y_pred)
loss = K.eval(loss)
loss = np.mean(loss)
acc = categorical_accuracy(y_true, y_pred)
acc = K.eval(acc)
acc = np.mean(acc)
print('Test loss:', loss)
print('Test accuracy:', acc)
def target_acc(y_true, y_pred, targetCols=(59, 62), val=False):
tars = y_true[:, :, targetCols[0]:targetCols[1]]
preds = y_pred[:, :, targetCols[0]:targetCols[1]]
if not val:
return metrics.categorical_accuracy(tars, preds)
else:
#This only works for a single example at a time
tars = tars[0, 0, :]
preds = preds[0, 0, :]
if np.argmax(tars) == np.argmax(preds):
return 1
else:
return 0
def custom_acc(self, y_true, y_pred):
y_true_shape = (-1, self.grid_size, self.grid_size,
self.bbox_params + len(self.classes))
y_pred_shape = (-1, self.grid_size, self.grid_size,
self.bbox_count * self.bbox_params + len(self.classes))
y_true = tf.reshape(y_true, y_true_shape, name='reshape_y_true')
y_pred = tf.reshape(y_pred, y_pred_shape, name='reshape_y_pred')
# shape=(?, 21, 21, 10),
predicted_class_prob = y_pred[:, :, :, 10:]
true_class_prob = y_true[:, :, :, 5:]
return categorical_accuracy(true_class_prob, predicted_class_prob)
def accuracy(package_data, p_class):
from keras.metrics import categorical_accuracy
g_class = tf.gather(package_data, indices=[0], axis=-1)
mask = tf.reshape(
tf.equal(g_class, Btype.NEG) | tf.equal(g_class, Btype.POSITIVE), [-1])
p_class = tf.boolean_mask(p_class, mask)
cate = tf.cast(tf.reshape(tf.equal(g_class, Btype.POSITIVE), [-1]),
tf.int32)
cate = tf.boolean_mask(cate, mask)
cate_one_hot = tf.reshape(tf.one_hot(cate, tf.constant(2, tf.int32)),
[-1, 2])
return categorical_accuracy(cate_one_hot, tf.reshape(p_class, [-1, 2]))
def padded_categorical_accuracy(y_true, y_pred):
"""Accuracy of a batch, ignoring padding.
>>> sh = [1, 3, 3] # 1 batch size x 3 time steps x 3 categories
>>> true = tf.constant([0., 0., 1., 0., 0., 1., 1., 0., 0.], shape=sh)
<[2, 2, 0 (padding)]>
>>> pred = tf.constant([0, 0.7, 0.3, 0, 0.3, 0.7, 0.5, 0.3, 0.2], shape=sh)
<[2, 1, 0)]>
>>> padded_categorical_accuracy(true, pred).eval()) # => 0.5
"""
padded = tf.squeeze(tf.slice(y_true, [0, 0, 0], [-1, -1, 1]), axis=2)
mask = K.equal(padded, 0.)
return categorical_accuracy(tf.boolean_mask(y_true, mask),
tf.boolean_mask(y_pred, mask))
def model_eval(x, y, model, X_test, Y_test, back='th'):
"""
Compute the accuracy of a TF model on some data
:param x: input placeholder
:param y: output placeholder (for labels)
:param model: model output predictions
:param X_test: numpy array with training inputs
:param Y_test: numpy array with training outputs
:return: a float with the accuracy value
"""
# Define sympbolic for accuracy
input_shape = (None, FLAGS.img_rows, FLAGS.img_cols)
acc_value = categorical_accuracy(y, model)
acc_value = K.function([x, y, K.learning_phase()], [acc_value])
# Init result var
accuracy = 0.0
# Compute number of batches
nb_batches = int(math.ceil(float(len(X_test)) / FLAGS.batch_size))
assert nb_batches * FLAGS.batch_size >= len(X_test)
for batch in range(nb_batches):
if batch % 100 == 0 and batch > 0:
print("Batch " + str(batch))
# Must not use the `batch_indices` function here, because it
# repeats some examples.
# It's acceptable to repeat during training, but not eval.
start = batch * FLAGS.batch_size
end = min(len(X_test), start + FLAGS.batch_size)
cur_batch_size = end - start + 1
# The last batch may be smaller than all others, so we need to
# account for variable batch size here
if back == 'tf':
accuracy += cur_batch_size * acc_value.eval(feed_dict={
x: X_test[start:end],
y: Y_test[start:end]
})
elif back == 'th':
accuracy += cur_batch_size * acc_value(
[X_test[start:end], Y_test[start:end], 0])[0]
assert end >= len(X_test)
# Divide by number of examples to get final value
accuracy /= len(X_test)
return accuracy
def accuracy(self, x, t):
x_ph = tf.placeholder(tf.float32, shape=[None, self.image_size])
t_ph = tf.placeholder(tf.float32, shape=[None, self.n_classes])
y_op = self._encode_y_given_x(x_ph)
acc_value = categorical_accuracy(t_ph, y_op)
with tf.Session() as sess:
if self.filepath:
self.saver.restore(sess, self.filepath)
result = self.sess.run(acc_value,
feed_dict={
x_ph: x,
t_ph: t,
K.learning_phase(): 1
})
return result
def evaluate_ensemble(Best=True):
'''
loads and evaluates an ensemle from the models in the model folder.
'''
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_test = X_test.reshape(10000, 784)
X_test = X_test.astype('float32')
X_test /= 255
Y_test = np_utils.to_categorical(y_test, 10)
model_dirs = []
for i in os.listdir('weights'):
if '.h5' in i:
if not Best:
model_dirs.append(i)
else:
if 'Best' in i:
model_dirs.append(i)
preds = []
model = create_model()
for mfile in model_dirs:
print(os.path.join('weights', mfile))
model.load_weights(os.path.join('weights', mfile))
yPreds = model.predict(X_test, batch_size=128, verbose=0)
preds.append(yPreds)
weighted_predictions = np.zeros((X_test.shape[0], 10), dtype='float64')
weight = 1. / len(preds)
for prediction in preds:
weighted_predictions += weight * prediction
y_pred = weighted_predictions
print(type(Y_test))
print(type(y_pred))
Y_test = tf.convert_to_tensor(Y_test)
y_pred = tf.convert_to_tensor(y_pred)
print(type(Y_test))
print(type(y_pred))
loss = metrics.categorical_crossentropy(Y_test, y_pred)
acc = metrics.categorical_accuracy(Y_test, y_pred)
sess = tf.Session()
print('--------------------------------------')
print('ensemble')
print('Test loss:', loss.eval(session=sess))
print('error:', str((1. - acc.eval(session=sess)) * 100) + '%')
print('--------------------------------------')
def __init__(self, Nin, Nh_l, Nout):
self.X_ph = tf.placeholder(tf.float32, shape=(None, Nin))
self.L_ph = tf.placeholder(tf.float32, shape=(None, Nout))
# Modeling
H = Dense(Nh_l[0], activation='relu')(self.X_ph)
H = Dropout(0.5)(H)
H = Dense(Nh_l[1], activation='relu')(H)
H = Dropout(0.25)(H)
self.Y_tf = Dense(Nout, activation='softmax')(H)
# Operation
self.Loss_tf = tf.reduce_mean(
categorical_crossentropy(self.L_ph, self.Y_tf))
self.Train_tf = tf.train.AdamOptimizer().minimize(self.Loss_tf)
self.Acc_tf = categorical_accuracy(self.L_ph, self.Y_tf)
self.Init_tf = tf.global_variables_initializer()
def L_acc(y_true, y_pred):
# Localization accuracy: a match is registered only if class is correctly identified with IOU > 0.6
# Confidence component (aka objectness)
y_true_confidence = y_true[:, 0]
y_pred_confidence = K.round(K.sigmoid(y_pred[:, 0]))
# Class component
y_true_classes = y_true[:, 4:]
y_pred_classes = K.softmax(y_pred[:, 4:])
classes_accuracy = categorical_accuracy(y_true_classes, y_pred_classes)
true_xy = y_true[:, 1:3]
pred_xy = K.sigmoid(y_pred[:, 1:3])
true_r = y_true[:, 3:4]
pred_r = K.exp(y_pred[:, 3:4])
# compute IOU, using the top,left,bottom,right representation.
true_mins = true_xy - true_r
true_maxes = true_xy + true_r
pred_mins = pred_xy - pred_r
pred_maxes = pred_xy + pred_r
intersect_mins = K.maximum(pred_mins, true_mins)
intersect_maxes = K.minimum(pred_maxes, true_maxes)
intersect_wh = K.maximum(intersect_maxes - intersect_mins, 0.)
intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]
pred_areas = 4. * pred_r[..., 0] * pred_r[..., 0] # a square
true_areas = 4. * true_r[..., 0] * true_r[..., 0]
union_areas = pred_areas + true_areas - intersect_areas
iou_scores = intersect_areas / union_areas
iou_accuracy = K.cast(iou_scores > 0.6, K.floatx())
joint_accuracy = (y_true_confidence * y_pred_confidence * classes_accuracy * iou_accuracy) + \
((1.0 - y_true_confidence) * (1.0 - y_pred_confidence)) # if background, dont care about class or iou
return joint_accuracy
def test_model(preds, in_images, test_files, chunk_size=64, shuffle=True):
"""Test the model"""
import tensorflow as tf
from keras import backend as K
from keras.objectives import binary_crossentropy
import numpy as np
from keras.metrics import categorical_accuracy
from tqdm import tqdm
in_labels = tf.placeholder(tf.float32, shape=(None, 2))
cross_entropy = tf.reduce_mean(binary_crossentropy(in_labels, preds))
accuracy = tf.reduce_mean(categorical_accuracy(in_labels, preds))
auc = tf.metrics.auc(tf.cast(in_labels, tf.bool), preds)
n_test_events = count_events(test_files)
chunk_num = int(n_test_events/chunk_size)+1
preds_all = []
label_all = []
sess = tf.get_default_session()
sess.run(tf.local_variables_initializer())
avg_accuracy = 0
avg_auc = 0
avg_test_loss = 0
is_training = tf.get_default_graph().get_tensor_by_name('is_training:0')
for img_chunk, label_chunk, real_chunk_size in tqdm(chunks(test_files, chunk_size, shuffle=shuffle),total=chunk_num):
test_loss, accuracy_result, auc_result, preds_result = sess.run([cross_entropy, accuracy, auc, preds],
feed_dict={in_images: img_chunk,
in_labels: label_chunk,
K.learning_phase(): 0,
is_training: False})
avg_test_loss += test_loss * real_chunk_size / n_test_events
avg_accuracy += accuracy_result * real_chunk_size / n_test_events
avg_auc += auc_result[0] * real_chunk_size / n_test_events
preds_all.extend(preds_result)
label_all.extend(label_chunk)
print("test_loss = ", "{:.3f}".format(avg_test_loss))
print("Test Accuracy:", "{:.3f}".format(avg_accuracy), ", Area under ROC curve:", "{:.3f}".format(avg_auc))
return avg_test_loss, avg_accuracy, avg_auc, np.asarray(preds_all).reshape(n_test_events,2), np.asarray(label_all).reshape(n_test_events,2)
def recall(y_true, y_pred, num_aspect_ratios, num_classes):
'''
Out of all the default boxes that are not background, how many does the model get right
Parameters same as loss_with_negative_mining
:param y_true:
:param y_pred:
:param num_aspect_ratios:
:param num_classes:
:return:
'''
zero = tf.constant(0, dtype=tf.int32)
# print "y_true", y_true, "y_pred", y_pred
# remove extra zero padding
y_true = tf.slice(y_true,
begin=[0, 0, 0, 0],
size=[-1, -1, -1, num_aspect_ratios])
y_pred_shape = tf.shape(y_pred)
y_pred = tf.reshape(y_pred,
shape=[
y_pred_shape[0], y_pred_shape[1], y_pred_shape[2],
num_aspect_ratios, num_classes
])
# print "After reshape and slicing: y_true", y_true, "y_pred", y_pred
y_pred = tf.reshape(y_pred, shape=[-1, num_classes])
y_true = tf.reshape(y_true, shape=[-1])
y_true = tf.cast(y_true, tf.int32)
pos_indices = tf.squeeze(tf.where(tf.not_equal(y_true, zero)))
print(pos_indices)
y_true_pos = tf.gather(y_true, pos_indices)
y_pred_pos = tf.gather(y_pred, pos_indices) #y_pred[pos_indices]
y_true_pos_one_hot = tf.one_hot(y_true_pos, depth=num_classes)
return categorical_accuracy(y_true_pos_one_hot, y_pred_pos)
def train(self):
self.classifier = self.create_model()
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(-1, 28, 28, 1)
x_test = x_test.reshape(-1, 28, 28, 1)
y_train = to_categorical(y_train, num_classes=10)
y_test = to_categorical(y_test, num_classes=10)
callbacks = [
EarlyStopping(monitor='val_loss', min_delta=1e-6, patience=10)
]
self.classifier.fit(x=x_train,
y=y_train,
batch_size=100,
epochs=50,
validation_split=0.1,
callbacks=callbacks)
self.classifier.save('classifier.h5')
predictions = self.classifier.predict(x_test)
y_pred = K.constant(predictions)
y_true = K.constant(y_test)
accuracy = np.mean(K.eval(categorical_accuracy(y_true, y_pred)))
print(accuracy)
return None
net = getattr(keras_helpers, args.network)()
data_shape = [224, 224, 3]
elif args.network == "GoogLeNet":
net = getattr(keras_helpers, args.network)()
data_shape = [224, 224, 3]
else:
sys.exit("Unknown Network")
fake_data = np.random.rand(args.train_batch, data_shape[0], data_shape[1], data_shape[2])
tmp_fake_labels = np.random.randint(0, high=1000, size=args.train_batch)
fake_labels = np.zeros([args.train_batch, 1000])
for i in range(args.train_batch):
fake_labels[i, tmp_fake_labels[i]] = 1
loss = categorical_crossentropy(net.y_, net.y)
top1 = categorical_accuracy(net.y_, net.y)
top5 = top_k_categorical_accuracy(net.y_, net.y, 5)
base_lr = 0.02
step = tf.Variable(0, trainable=False, name="Step")
learning_rate = tf.train.exponential_decay(base_lr, step, 1, 0.999964)
weight_list = [v for v in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) if v.name[-3:] == "W:0"]
bias_list = [v for v in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) if v.name[-3:] == "b:0"]
optimizer1 = tf.train.MomentumOptimizer(learning_rate, 0.9)
optimizer2 = tf.train.MomentumOptimizer(tf.scalar_mul(2.0, learning_rate), 0.9)
grads = optimizer1.compute_gradients(loss, var_list=weight_list+bias_list)
w_grads = grads[:len(weight_list)]
b_grads = grads[len(weight_list):]
def evaluate_cate(category_size, attribute_size, y_true, y_pred):
return categorical_accuracy(y_true[:, :category_size], y_pred[:, :category_size])