def load_model(model_name):
if model_name == 'deepyeast':
from deepyeast.models import DeepYeast
model = DeepYeast()
elif model_name == 'resnet':
from deepyeast.models import ResNet50
model = ResNet50()
elif model_name == 'mobilenet':
from deepyeast.models import MobileNet
model = MobileNet()
elif model_name == 'densenet':
from deepyeast.models import DenseNet40_BC
model = DenseNet40_BC()
return model
# increase lr
lr *= self.lr_mult
K.set_value(self.model.optimizer.lr, lr)
def plot(self):
plt.plot(self.lrs, self.losses)
plt.xscale("log")
plt.xlabel("learning rate (log scale)")
plt.ylabel("loss")
if __name__ == "__main__":
# set up data
x_train, y_train = load_data("train")
num_classes = 12
y_train = keras.utils.to_categorical(y_train, num_classes)
x_train = preprocess_input(x_train)
# set up model
model = DeepYeast()
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.SGD())
# search for lr
lr_finder = LearningRateFinder(model)
lr_finder.find()
lr_finder.plot()
from deepyeast.utils import preprocess_input
from deepyeast.models import DeepYeast
# set up data
x_val, y_val = load_data("val")
x_train, y_train = load_data("train")
num_classes = 12
y_val = keras.utils.to_categorical(y_val, num_classes)
y_train = keras.utils.to_categorical(y_train, num_classes)
x_train = preprocess_input(x_train)
x_val = preprocess_input(x_val)
# set up model
model = DeepYeast()
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adam(),
metrics=['accuracy'])
filepath = "../weights-{epoch:02d}-{val_acc:.3f}.hdf5"
checkpoint = keras.callbacks.ModelCheckpoint(filepath,
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_acc',
factor=0.1,
patience=5,
cooldown=0,
import numpy as np
import keras.backend as K
from keras.layers import Activation, GlobalAveragePooling2D
from skimage import exposure
import matplotlib.pyplot as plt
from deepyeast.dataset import load_data
from deepyeast.utils import preprocess_input
from deepyeast.models import DeepYeast
### select model
model = DeepYeast()
model.load_weights("weights/deepyeast-weights-22-0.902.hdf5")
### plot image
def stretch_contrast(img):
p2, p98 = np.percentile(img, (2, 98))
img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98))
return img_rescale
def add_third_dimension(x):
b_channel = np.zeros((64, 64, 1), dtype=np.uint8)
x = np.append(x, b_channel, axis=-1)
return x
x_val, y_val = load_data("val")
i = 0
from deepyeast.utils import preprocess_input, mixup_generator
from deepyeast.models import DeepYeast
# set up data
x_train, y_train = load_data("train")
x_val, y_val = load_data("val")
num_classes = 12
y_train = keras.utils.to_categorical(y_train, num_classes)
y_val = keras.utils.to_categorical(y_val, num_classes)
x_train = preprocess_input(x_train)
x_val = preprocess_input(x_val)
# set up model
model = DeepYeast()
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adam(),
metrics=['accuracy'])
filepath = "weights-{epoch:02d}-{val_acc:.3f}.hdf5"
checkpoint = keras.callbacks.ModelCheckpoint(filepath,
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_acc',
factor=0.1,
patience=5,
cooldown=0,