aaa = 1
x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], x_train.shape[2],
aaa)
x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2], aaa)
print(x_train.shape, y_train.shape) # (3628, 128, 862, 1) (3628,)
print(x_test.shape, y_test.shape) # (908, 128, 862, 1) (908,)
model = MobileNet(
include_top=True,
input_shape=(128, 862, 1),
classes=2,
pooling=None,
weights=None,
)
model.summary()
# model.trainable = False
# model.save('C:/nmb/nmb_data/h5/5s/mobilenet_rmsprop_1.h5')
# 컴파일, 훈련
op = RMSprop(lr=1e-3)
batch_size = 8
es = EarlyStopping(monitor='val_loss',
patience=20,
restore_best_weights=True,
verbose=1)
lr = ReduceLROnPlateau(monitor='val_loss', vactor=0.5, patience=10, verbose=1)
path = 'C:/nmb/nmb_data/h5/5s/mobilenet/mobilenet_rmsprop_1.h5'
mc = ModelCheckpoint(path, monitor='val_loss', verbose=1, save_best_only=True)
model.compile(optimizer=op,
loss="sparse_categorical_crossentropy",
def train(input_params, train, test, valid, class_cnt):
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
# tensorboard
train_log_dir = 'logs/gradient_tape/' + current_time + '/train'
valid_log_dir = 'logs/gradient_tape/' + current_time + '/valid'
test_log_dir = 'logs/gradient_tape/' + current_time + '/test'
train_summary_writer = tf.summary.create_file_writer(train_log_dir)
valid_summary_writer = tf.summary.create_file_writer(valid_log_dir)
# test_summary_writer = tf.summary.create_file_writer(test_log_dir)
# todo: create model with hyperparams with model_dir = '../data/models/params/current_time/'
model_dir = '../data/models/model-' + current_time
# Instantiate an optimizer.
optimizer = Adam(learning_rate=0.001)
# Instantiate a loss function.
loss_fn = SparseCategoricalCrossentropy(from_logits=True)
train_step = test_step = 0
# Prepare the metrics.
#todo use same variable for all the acc_metrics.
acc_metric = SparseCategoricalAccuracy()
if utility.dir_empty(model_dir):
# model definition
mobilenet = MOBILENET(include_top=False,
input_shape=(224, 224, 3),
weights='imagenet',
pooling='avg',
dropout=0.001)
mobilenet.summary()
# select till which layer use mobilenet.
base_model = Model(inputs=mobilenet.input, outputs=mobilenet.output)
base_model.summary()
model = Sequential([
base_model,
Dropout(0.2),
Dense(units=class_cnt, activation='softmax'),
])
model.summary()
epochs = 200
for epoch in range(epochs):
print("\nStart of epoch %d" % (epoch,))
for batch_idx, (x_batch_train, y_batch_train) in enumerate(train):
with tf.GradientTape() as tape:
# forward pass
logits = model(x_batch_train, training=True)
# compute loss for mini batch
loss_value = loss_fn(y_batch_train, logits)
grads = tape.gradient(loss_value, model.trainable_weights)
optimizer.apply_gradients(zip(grads, model.trainable_weights))
# Update training metric.
acc_metric.update_state(y_batch_train, logits)
with train_summary_writer.as_default():
# import code; code.interact(local=dict(globals(), **locals()))
#TODO: add the metrics for test too.
#TODO: take the mean of the losses in every batch and then show,
#TODO loss_value is last loss of the batch(only 1).
tf.summary.scalar('loss', loss_value, step=train_step)
tf.summary.scalar('accuracy', acc_metric.result(), step=train_step)
train_step += 1
if batch_idx % 10 == 0:
print("training loss for one batch at step %d: %.4f" % (batch_idx, float(loss_value)))
# Display metrics at the end of each epoch.
print("Training acc over epoch: %.4f" % (float(acc_metric.result()),))
# Reset training metrics at the end of each epoch
acc_metric.reset_states()
# iterate on validation
for batch_idx, (x_batch_val, y_batch_val) in enumerate(valid):
# val_logits: y_pred of the validation.
val_logits = model(x_batch_val, training=False)
loss = loss_fn(y_batch_val, val_logits)
# Update val metrics
acc_metric.update_state(y_batch_val, val_logits)
with valid_summary_writer.as_default():
tf.summary.scalar('loss', loss, step=test_step)
tf.summary.scalar('accuracy', acc_metric.result(), step=test_step)
test_step += 1
print("Validation acc: %.4f" % (float(acc_metric.result()),))
# print(classification_report(y_batch_val, val_logits, target_names=labels))
acc_metric.reset_states()
acc_metric.reset_states()
model.save(model_dir + 'model')
else: # if model_dir is not empty
print("model already exist. loading model...")
model = load_model(model_dir+'model')
"""
return np.mean([apk(a, p, k) for a, p in zip(actual, predicted)])
def preds2catids(predictions):
return pd.DataFrame(np.argsort(-predictions, axis=1)[:, :3], columns=['a', 'b', 'c'])
def top_3_accuracy(y_true, y_pred):
return top_k_categorical_accuracy(y_true, y_pred, k=3)
STEPS = 800
EPOCHS = 16
size = 64
batchsize = 680
model = MobileNet(input_shape=(size, size, 1), alpha=1., weights=None, classes=NCATS)
model.compile(optimizer=Adam(lr=0.002), loss='categorical_crossentropy',
metrics=[categorical_crossentropy, categorical_accuracy, top_3_accuracy])
print(model.summary())
def draw_cv2(raw_strokes, size=256, lw=6, time_color=True):
img = np.zeros((BASE_SIZE, BASE_SIZE), np.uint8)
for t, stroke in enumerate(raw_strokes):
for i in range(len(stroke[0]) - 1):
color = 255 - min(t, 10) * 13 if time_color else 255
_ = cv2.line(img, (stroke[0][i], stroke[1][i]),
(stroke[0][i + 1], stroke[1][i + 1]), color, lw)
if size != BASE_SIZE:
return cv2.resize(img, (size, size))
else:
return img
def image_generator_xd(size, batchsize, ks, lw=6, time_color=True):
while True:
for k in np.random.permutation(ks):
def mobilenet_seg_model(pretrained_weights=None, input_size=(256, 256, 3)):
model = MobileNet(input_shape=input_size,
weights='imagenet',
include_top=False)
x = model.output
encoder = Model(inputs=model.input, outputs=x)
skip_layer_1 = encoder.get_layer('conv_pw_1_relu').output
skip_layer_2 = encoder.get_layer('conv_pw_3_relu').output
skip_layer_3 = encoder.get_layer('conv_pw_5_relu').output
skip_layer_4 = encoder.get_layer('conv_pw_11_relu').output
up6 = Conv2D(
512,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(
x) #(UpSampling2D(size = (2,2), interpolation='bilinear')(x))
up6 = Conv2DTranspose(512,
2,
activation='relu',
padding='same',
strides=2,
kernel_initializer='he_normal')(up6)
merge6 = concatenate([skip_layer_4, up6], axis=3)
conv6 = Conv2D(512,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge6)
conv6 = Conv2D(512,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv6)
up7 = Conv2D(
256,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal'
)(conv6) #(UpSampling2D(size = (2,2), interpolation='bilinear')(conv6))
up7 = Conv2DTranspose(256,
2,
activation='relu',
padding='same',
strides=2,
kernel_initializer='he_normal')(up7)
merge7 = concatenate([skip_layer_3, up7], axis=3)
conv7 = Conv2D(256,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge7)
conv7 = Conv2D(256,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv7)
up8 = Conv2D(
128,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal'
)(conv7) #(UpSampling2D(size = (2,2), interpolation='bilinear')(conv7))
up8 = Conv2DTranspose(128,
2,
activation='relu',
padding='same',
strides=2,
kernel_initializer='he_normal')(up8)
merge8 = concatenate([skip_layer_2, up8], axis=3)
conv8 = Conv2D(128,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge8)
conv8 = Conv2D(128,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv8)
up9 = Conv2D(
64,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal'
)(conv8) #(UpSampling2D(size = (2,2), interpolation='bilinear')(conv8))
up9 = Conv2DTranspose(64,
2,
activation='relu',
padding='same',
strides=2,
kernel_initializer='he_normal')(up9)
merge9 = concatenate([skip_layer_1, up9], axis=3)
conv9 = Conv2D(64,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge9)
conv9 = Conv2D(64,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv9)
up10 = Conv2D(
32,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal'
)(conv9) #(UpSampling2D(size = (2,2), interpolation='bilinear')(conv9))
up10 = Conv2DTranspose(32,
2,
activation='relu',
padding='same',
strides=2,
kernel_initializer='he_normal')(up10)
#merge9 = concatenate([conv1,up9], axis = 3)
conv10 = Conv2D(32,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(up10)
conv10 = Conv2D(32,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv10)
conv10 = Conv2D(16,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv10)
conv10 = Conv2D(1, 1, activation='sigmoid')(conv10)
model = Model(inputs=model.input, outputs=conv10)
#model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics=[my_IoU])
#model.compile(optimizer = Adam(lr = 1e-4), loss = dice_coef_loss, metrics=[my_IoU])
model.compile(optimizer=Adam(lr=1e-4),
loss=weighted_bce_dice_loss,
metrics=[my_IoU])
model.summary()
if (pretrained_weights is not None):
model.load_weights(pretrained_weights)
return model
