def evaluate(split):
print('Loading {} set...'.format(split))
x, y_true = load_data(split)
x = preprocess_input(x)
y_pred = model.predict(x)
y_pred = y_pred.argmax(axis=1)
print("{} set statistics:".format(split))
print("Top-1-accuracy: {:.4f}".format(np.mean(y_true == y_pred)))
print(metrics.classification_report(y_true, y_pred))
def grad_cam(x, class_ix, layer):
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
last_conv_features = model.get_layer(layer).output
class_prob = model.get_layer("predictions").output
grads = K.gradients(class_prob[:, class_ix], last_conv_features)[0]
weights = GlobalAveragePooling2D()(Activation("relu")(grads))
grad_cam_fn = K.function([model.input, K.learning_phase()],
[last_conv_features, weights])
learning_phase = 0
out = grad_cam_fn([x, learning_phase])
heatmap = out[0] * out[1].reshape(1, 1, 1, out[1].shape[-1])
heatmap = np.sum(heatmap, axis=3)
return heatmap
# 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()
import keras
from deepyeast.dataset import load_data
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',