def main():
trainX = get_salt_images(folder='mytrain')
train_pix_num = sd.salt_pixel_num(folder='mytrain')
trainY = np.array(train_pix_num > 0).astype(int)
valX = get_salt_images(folder='myval')
val_pix_num = sd.salt_pixel_num(folder='myval')
valY = np.array(val_pix_num > 0).astype(int)
# Build the Estimator
model_dir = '/scratch0/ilya/locDoc/data/kaggle-seismic-dataset/models/binaryhalf1'
model = tf.estimator.Estimator(model_fn, model_dir=model_dir)
for i in range(100000):
# Define the input function for training
input_fn = tf.estimator.inputs.numpy_input_fn(
x={'images': trainX[:3584,:,:,:]}, y=trainY[:3584],
batch_size=batch_size, num_epochs=1, shuffle=True)
# Train the Model
tf.logging.set_verbosity(tf.logging.INFO)
model.train(input_fn, steps=None)
# if i % 2 == 0:
# Evaluate the Model
# Define the input function for evaluating
input_fn = tf.estimator.inputs.numpy_input_fn(
x={'images': valX[:384,:,:,:]}, y=valY[:384],
batch_size=batch_size, shuffle=False)
# Use the Estimator 'evaluate' method
e = model.evaluate(input_fn)
print("Testing Accuracy:", e['accuracy'])
def view_mistakes():
foldername = 'myval'
id_to_pred = get_decisions(
foldername,
'/scratch0/ilya/locDoc/data/kaggle-seismic-dataset/models/binary13b')
fileids = sb.clean_glob(
glob.glob(
'/scratch0/ilya/locDoc/data/kaggle-seismic-dataset/%s/images/*.png'
% foldername))
# imgs = sd.get_salt_labels(folder='myval')
# imgs = np.reshape(imgs, (imgs.shape[0], 101,101))
imgs = sd.get_salt_images(folder='myval')
val_pix_num = sd.salt_pixel_num(folder='myval')
valY = np.array(val_pix_num > 0).astype(int)
mistake_i = 0
for file_id_i, file_id in enumerate(fileids):
fileid, file_extension = os.path.splitext(file_id)
if valY[file_id_i] != id_to_pred[fileid]:
imgs[mistake_i, :, :] = imgs[file_id_i, :, :]
mistake_i += 1
print('made %i mistakes' % mistake_i)
X = np.swapaxes(np.swapaxes(imgs[:mistake_i, :, :], 0, 2), 0,
1).astype(float)
fig, ax = plt.subplots(1, 1)
tracker = ScrollThruPlot(ax, X, fig)
fig.canvas.mpl_connect('scroll_event', tracker.onscroll)
plt.show()