diff options
author | 2018-01-25 12:02:36 -0800 | |
---|---|---|
committer | 2018-01-25 12:07:22 -0800 | |
commit | 351c0a533a111636333b4ebeede16485cf679ca9 (patch) | |
tree | a0786bc9a8fe7432d69d8095b10586e3ef515b93 /tensorflow/examples | |
parent | a8c4e8d96de7c0978851a5f9718bbd6b8056d862 (diff) |
Add C0330 bad-continuation check to pylint.
PiperOrigin-RevId: 183270896
Diffstat (limited to 'tensorflow/examples')
-rw-r--r-- | tensorflow/examples/how_tos/reading_data/fully_connected_reader.py | 49 | ||||
-rw-r--r-- | tensorflow/examples/label_image/label_image.py | 41 |
2 files changed, 46 insertions, 44 deletions
diff --git a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py index fa4c1c0da5..461fb1c517 100644 --- a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py +++ b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Train and Eval the MNIST network. This version is like fully_connected_feed.py but uses data converted @@ -65,6 +64,7 @@ def decode(serialized_example): return image, label + def augment(image, label): # OPTIONAL: Could reshape into a 28x28 image and apply distortions # here. Since we are not applying any distortions in this @@ -72,12 +72,14 @@ def augment(image, label): # into a vector, we don't bother. return image, label + def normalize(image, label): # Convert from [0, 255] -> [-0.5, 0.5] floats. image = tf.cast(image, tf.float32) * (1. / 255) - 0.5 return image, label + def inputs(train, batch_size, num_epochs): """Reads input data num_epochs times. @@ -98,9 +100,10 @@ def inputs(train, batch_size, num_epochs): over the dataset once. On the other hand there is no special initialization required. """ - if not num_epochs: num_epochs = None - filename = os.path.join(FLAGS.train_dir, - TRAIN_FILE if train else VALIDATION_FILE) + if not num_epochs: + num_epochs = None + filename = os.path.join(FLAGS.train_dir, TRAIN_FILE + if train else VALIDATION_FILE) with tf.name_scope('input'): # TFRecordDataset opens a protobuf and reads entries line by line @@ -127,13 +130,11 @@ def run_training(): # Tell TensorFlow that the model will be built into the default Graph. with tf.Graph().as_default(): # Input images and labels. - image_batch, label_batch = inputs(train=True, batch_size=FLAGS.batch_size, - num_epochs=FLAGS.num_epochs) + image_batch, label_batch = inputs( + train=True, batch_size=FLAGS.batch_size, num_epochs=FLAGS.num_epochs) # Build a Graph that computes predictions from the inference model. - logits = mnist.inference(image_batch, - FLAGS.hidden1, - FLAGS.hidden2) + logits = mnist.inference(image_batch, FLAGS.hidden1, FLAGS.hidden2) # Add to the Graph the loss calculation. loss = mnist.loss(logits, label_batch) @@ -152,7 +153,7 @@ def run_training(): sess.run(init_op) try: step = 0 - while True: #train until OutOfRangeError + while True: #train until OutOfRangeError start_time = time.time() # Run one step of the model. The return values are @@ -168,10 +169,12 @@ def run_training(): # Print an overview fairly often. if step % 100 == 0: print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, - duration)) + duration)) step += 1 except tf.errors.OutOfRangeError: - print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step)) + print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, + step)) + def main(_): run_training() @@ -183,37 +186,27 @@ if __name__ == '__main__': '--learning_rate', type=float, default=0.01, - help='Initial learning rate.' - ) + help='Initial learning rate.') parser.add_argument( '--num_epochs', type=int, default=2, - help='Number of epochs to run trainer.' - ) + help='Number of epochs to run trainer.') parser.add_argument( '--hidden1', type=int, default=128, - help='Number of units in hidden layer 1.' - ) + help='Number of units in hidden layer 1.') parser.add_argument( '--hidden2', type=int, default=32, - help='Number of units in hidden layer 2.' - ) - parser.add_argument( - '--batch_size', - type=int, - default=100, - help='Batch size.' - ) + help='Number of units in hidden layer 2.') + parser.add_argument('--batch_size', type=int, default=100, help='Batch size.') parser.add_argument( '--train_dir', type=str, default='/tmp/data', - help='Directory with the training data.' - ) + help='Directory with the training data.') FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/examples/label_image/label_image.py b/tensorflow/examples/label_image/label_image.py index d62b73384c..1c1bd57d71 100644 --- a/tensorflow/examples/label_image/label_image.py +++ b/tensorflow/examples/label_image/label_image.py @@ -23,6 +23,7 @@ import sys import numpy as np import tensorflow as tf + def load_graph(model_file): graph = tf.Graph() graph_def = tf.GraphDef() @@ -34,22 +35,26 @@ def load_graph(model_file): return graph -def read_tensor_from_image_file(file_name, input_height=299, input_width=299, - input_mean=0, input_std=255): + +def read_tensor_from_image_file(file_name, + input_height=299, + input_width=299, + input_mean=0, + input_std=255): input_name = "file_reader" output_name = "normalized" file_reader = tf.read_file(file_name, input_name) if file_name.endswith(".png"): - image_reader = tf.image.decode_png(file_reader, channels = 3, - name='png_reader') + image_reader = tf.image.decode_png( + file_reader, channels=3, name="png_reader") elif file_name.endswith(".gif"): - image_reader = tf.squeeze(tf.image.decode_gif(file_reader, - name='gif_reader')) + image_reader = tf.squeeze( + tf.image.decode_gif(file_reader, name="gif_reader")) elif file_name.endswith(".bmp"): - image_reader = tf.image.decode_bmp(file_reader, name='bmp_reader') + image_reader = tf.image.decode_bmp(file_reader, name="bmp_reader") else: - image_reader = tf.image.decode_jpeg(file_reader, channels = 3, - name='jpeg_reader') + image_reader = tf.image.decode_jpeg( + file_reader, channels=3, name="jpeg_reader") float_caster = tf.cast(image_reader, tf.float32) dims_expander = tf.expand_dims(float_caster, 0) resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width]) @@ -59,6 +64,7 @@ def read_tensor_from_image_file(file_name, input_height=299, input_width=299, return result + def load_labels(label_file): label = [] proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines() @@ -66,6 +72,7 @@ def load_labels(label_file): label.append(l.rstrip()) return label + if __name__ == "__main__": file_name = "tensorflow/examples/label_image/data/grace_hopper.jpg" model_file = \ @@ -110,11 +117,12 @@ if __name__ == "__main__": output_layer = args.output_layer graph = load_graph(model_file) - t = read_tensor_from_image_file(file_name, - input_height=input_height, - input_width=input_width, - input_mean=input_mean, - input_std=input_std) + t = read_tensor_from_image_file( + file_name, + input_height=input_height, + input_width=input_width, + input_mean=input_mean, + input_std=input_std) input_name = "import/" + input_layer output_name = "import/" + output_layer @@ -122,8 +130,9 @@ if __name__ == "__main__": output_operation = graph.get_operation_by_name(output_name) with tf.Session(graph=graph) as sess: - results = sess.run(output_operation.outputs[0], - {input_operation.outputs[0]: t}) + results = sess.run(output_operation.outputs[0], { + input_operation.outputs[0]: t + }) results = np.squeeze(results) top_k = results.argsort()[-5:][::-1] |