process in which ideas and objects are recognized, differentiated, and understood.” - Wikipedia “Statistical classification, identifies to which of a set of categories a new observation belongs, on the basis of a training set of data.” - Wikipedia
algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions” - Wikipedia
computation using data flow graphs Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them”
graph (DAG) to optimize an objective function • Graph is defined in high-level language • Graph is compiled and optimized • Graph is executed on available low level devices (CPU, GPU) • Data flow through the graph
a dataflow graph. ◦ collection of operations that can be executed together as a group • Operation: a graph node that performs computation on tensors • Tensor: a handle to one of the output of an operation ◦ provides a means of computing the value in a TensorFlow Session
with data on execution • Variables: modifiable tensors that live in TensorFlow’s graph • Session: encapsulates the environment in which Operations are executed and Tensors are evaluated
tf.div division tf.mod module tf.abs absolute value tf.neg negative value tf.inv inverse tf.maximum returns the maximum tf.minimum returns the minimum Operation Description tf.square calculates the square tf.round nearest integer tf.sqrt square root tf.pow calculates the power tf.exp exponential tf.log logarithm tf.cos calculates the cosine tf.sin calculates the sine tf.matmul tensor product tf.transpose tensor transpose
produces a 1x2 matrix. The op is # added as a node to the default graph. # # The value returned by the constructor represents the output # of the Constant op. matrix1 = tf.constant([[3., 3.]]) # Create another Constant that produces a 2x1 matrix. matrix2 = tf.constant([[2.],[2.]]) # Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs. # The returned value, 'product', represents the result of the matrix # multiplication. product = tf.matmul(matrix1, matrix2)
run the matmul op we call the session 'run()' method, passing 'product' # which represents the output of the matmul op. This indicates to the call # that we want to get the output of the matmul op back. # # All inputs needed by the op are run automatically by the session. They # typically are run in parallel. # # The call 'run(product)' thus causes the execution of three ops in the # graph: the two constants and matmul. # # The output of the op is returned in 'result' as a numpy `ndarray` object. result = sess.run(product) print(result) # ==> [[ 12.]] # Close the Session when we're done. sess.close()
binary output • For each input x i there is a weight w i that determines how relevant the input x i is to the output • b is the bias and defines the activation threshold (credits: The Project Spot)
a large visual database designed for use in visual object recognition software research • Structured in a hierarchy in which each node is depicted by over five hundred images per node • As of 2016, over ten million URLs of images have been hand-annotated by ImageNet to indicate what objects are pictured • Since 2010, the ImageNet project runs an annual software contest where software programs compete to correctly classify and detect objects and scenes.
ensemble of four of these models achieves 3.58% top-5 error on the validation set of the ImageNet • In the 2015 ImageNet Challenge, an ensemble of 4 of these models came in 2nd in the image classification task
applying it to a different but related problem For example, the abilities acquired while learning to walk presumably apply when one learns to run, and knowledge gained while learning to recognize cars could apply when recognizing trucks” - Wikipedia
the layer just before the final output layer that actually does the classification. • Caches the outputs of the lower layers on disk so that they don't have to be repeatedly recalculated.
fit image_path = sys.argv[1] # Read in the image_data (the one which will be used for testing the NN) image_data = tf.gfile.FastGFile(image_path, 'rb').read() # Loads label file, strips off carriage return label_lines = [line.rstrip() for line in tf.gfile.GFile("/tf_files/retrained_labels.txt")] # Import a serialized GraphDef. GraphDef is a Graph definition, saved as a ProtoBuf with tf.gfile.FastGFile("/tf_files/retrained_graph.pb", 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) _ = tf.import_graph_def(graph_def, name='')
to the graph and get first prediction softmax_tensor = sess.graph.get_tensor_by_name('final_result:0') predictions = sess.run(softmax_tensor, \ {'DecodeJpeg/contents:0': image_data}) # Sort to show labels of first prediction in order of confidence top_k = predictions[0].argsort()[-len(predictions[0]):][::-1] for node_id in top_k: human_string = label_lines[node_id] score = predictions[0][node_id] print('%s (score = %.5f)' % (human_string, score))
rate to use when training. --train_batch_size How many images to train on at a time. --how_many_training_steps How many training steps to run before ending. --random_scale A percentage determining how much to randomly scale up the size of the training images by. --flip_left_right Whether to randomly flip half of the training images horizontally. --random_crop A percentage determining how much of a margin to randomly crop off the training images. --random_brightness A percentage determining how much to randomly multiply the training image input pixels up or down by. --testing_percentage What percentage of images to use as a test set. --validation_percentage What percentage of images to use as a validation set.
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