# Plot ad hoc OS data instances
from utils import load_labelled_patches, load_class_names
import matplotlib.pyplot as plt
from keras import backend as K
# load 4 randomly selected 128x128 patches and their labels
(X, y) = load_labelled_patches(["SU4010"], 128, limit=25, shuffle=True)
# load the list of possible labels
clznames = load_class_names()
#if we're using the theano backend, we need to change indexing order for matplotlib to interpret the patches:
if K.image_dim_ordering() == 'th':
X = X.transpose(0, 3, 1, 2)
# plot 4 images
for i in xrange(0, 4):
plt.subplot(2, 2, i + 1).set_title(clznames[y[i].argmax()])
plt.imshow(X[i])
# show the plot
plt.show()
from resnet50 import ResNet50
from imagenet_utils import preprocess_input
from keras.models import Model
from utils import load_labelled_patches
model = ResNet50(include_top=True, weights='imagenet')
# Get input
new_input = model.input
# Find the layer to end on
new_output = model.layers[-2].output
# Build a new model
newmodel = Model(new_input, new_output)
(X, y_test_true) = load_labelled_patches(["SU4012"],
224,
limit=4,
shuffle=True)
X = preprocess_input(X)
features = newmodel.predict(X)
print features.shape
print features
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Convolution2D
from keras.layers.convolutional import MaxPooling2D
import matplotlib.pylab as plt
from utils import generate_labelled_patches, load_labelled_patches, load_class_names
from keras import backend as K
# define the patch size as a variable so its easier to change later. For now,
# we'll set it to 28, just like the mnist images
patch_size = 28
# load data
train_data = generate_labelled_patches(["SU4010"], patch_size, shuffle=True)
valid_data = load_labelled_patches(["SU4011"],
patch_size,
subcoords=((0, 0), (300, 300)))
# load the class names
clznames = load_class_names()
num_classes = len(clznames)
class DisplayMap(keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs={}):
clzs = model.predict_classes(valid_data[0])
clzs = clzs.reshape((300 - patch_size, 300 - patch_size))
plt.figure()
plt.imshow(clzs)
plt.savefig("map_epoch%s.png" % epoch)
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Convolution2D
from keras.layers.convolutional import MaxPooling2D
import matplotlib.pylab as plt
from utils import generate_labelled_patches, load_labelled_patches, load_class_names
from keras import backend as K
# define the patch size as a variable so its easier to change later. For now,
# we'll set it to 28, just like the mnist images
patch_size = 28
# load data
train_data = generate_labelled_patches(["SU4010"], patch_size, shuffle=True)
valid_data = load_labelled_patches(["SU4011"],
patch_size,
limit=1000,
shuffle=True)
# load the class names
clznames = load_class_names()
num_classes = len(clznames)
def larger_model(input_shape, num_classes):
# create model
model = Sequential()
model.add(
Convolution2D(30, (5, 5),
padding='valid',
input_shape=input_shape,
activation='relu'))
new_output = Dense(num_classes)(hidden_layer)
# Build a new model
newmodel = Model(new_input, new_output)
return newmodel
model = hack_resnet(num_classes)
# the resnet expects 224x224 inputs
patch_size = 224
# load data
train_data = generate_labelled_patches(["SU4010"], patch_size, shuffle=True)
valid_data = load_labelled_patches(["SU4011"],
patch_size,
limit=1000,
shuffle=True)
# set weights in all but last layer
# to non-trainable (weights will not be updated)
for layer in model.layers[:len(model.layers) - 2]:
layer.trainable = False
# compile the model with a SGD/momentum optimizer
# and a very slow learning rate.
model.compile(loss='binary_crossentropy',
optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),
metrics=['accuracy'])
# fine-tune the model
model.fit_generator(train_data,
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Convolution2D
from keras.layers.convolutional import MaxPooling2D
import matplotlib.pylab as plt
from utils import generate_labelled_patches, load_labelled_patches, load_class_names
from keras import backend as K
# define the patch size as a variable so its easier to change later. For now,
# we'll set it to 28, just like the mnist images
patch_size = 28
# load data
train_data = generate_labelled_patches(["SU4010"], patch_size, shuffle=True)
valid_data = load_labelled_patches(["SU4011"],
patch_size,
limit=1000,
shuffle=True)
# load the class names
clznames = load_class_names()
num_classes = len(clznames)
def larger_model(input_shape, num_classes):
# create model
model = Sequential()
model.add(
Convolution2D(30, (5, 5),
padding='valid',
input_shape=input_shape,
activation='relu'))