/external/tensorflow/tensorflow/python/util/ |
H A D | future_api.py | 25 import tensorflow as tf namespace 28 delattr(tf, 'arg_max') 29 delattr(tf, 'arg_min') 30 delattr(tf, 'create_partitioned_variables') 31 delattr(tf, 'deserialize_many_sparse') 32 delattr(tf, 'lin_space') 33 delattr(tf, 'parse_single_sequence_example') 34 delattr(tf, 'serialize_many_sparse') 35 delattr(tf, 'serialize_sparse') 36 delattr(tf, 'sparse_matmu [all...] |
H A D | future_api_test.py | 20 import tensorflow as tf namespace 27 class ExampleParserConfigurationTest(tf.test.TestCase): 30 self.assertFalse(hasattr(tf, 'arg_max')) 31 self.assertTrue(hasattr(tf, 'argmax')) 35 tf.test.main()
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/external/tensorflow/tensorflow/examples/tutorials/mnist/ |
H A D | mnist.py | 35 import tensorflow as tf namespace 57 with tf.name_scope('hidden1'): 58 weights = tf.Variable( 59 tf.truncated_normal([IMAGE_PIXELS, hidden1_units], 62 biases = tf.Variable(tf.zeros([hidden1_units]), 64 hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases) 66 with tf.name_scope('hidden2'): 67 weights = tf [all...] |
H A D | mnist_deep.py | 35 import tensorflow as tf namespace 56 with tf.name_scope('reshape'): 57 x_image = tf.reshape(x, [-1, 28, 28, 1]) 60 with tf.name_scope('conv1'): 63 h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) 66 with tf.name_scope('pool1'): 70 with tf.name_scope('conv2'): 73 h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) 76 with tf.name_scope('pool2'): 81 with tf [all...] |
H A D | mnist_with_summaries.py | 18 tf.name_scope to make a graph legible in the TensorBoard graph explorer, and of 31 import tensorflow as tf namespace 43 sess = tf.InteractiveSession() 47 with tf.name_scope('input'): 48 x = tf.placeholder(tf.float32, [None, 784], name='x-input') 49 y_ = tf.placeholder(tf.int64, [None], name='y-input') 51 with tf.name_scope('input_reshape'): 52 image_shaped_input = tf [all...] |
H A D | mnist_softmax.py | 29 import tensorflow as tf namespace 39 x = tf.placeholder(tf.float32, [None, 784]) 40 W = tf.Variable(tf.zeros([784, 10])) 41 b = tf.Variable(tf.zeros([10])) 42 y = tf.matmul(x, W) + b 45 y_ = tf.placeholder(tf [all...] |
H A D | mnist_softmax_xla.py | 25 import tensorflow as tf namespace 38 x = tf.placeholder(tf.float32, [None, 784]) 39 w = tf.Variable(tf.zeros([784, 10])) 40 b = tf.Variable(tf.zeros([10])) 41 y = tf.matmul(x, w) + b 44 y_ = tf.placeholder(tf [all...] |
/external/tensorflow/tensorflow/user_ops/ |
H A D | invalid_op_test.py | 22 import tensorflow as tf namespace 25 class InvalidOpTest(tf.test.TestCase): 28 library_filename = os.path.join(tf.resource_loader.get_data_files_path(), 30 with self.assertRaises(tf.errors.InvalidArgumentError): 31 tf.load_op_library(library_filename) 35 tf.test.main()
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H A D | duplicate_op_test.py | 22 import tensorflow as tf namespace 25 class DuplicateOpTest(tf.test.TestCase): 28 library_filename = os.path.join(tf.resource_loader.get_data_files_path(), 30 duplicate = tf.load_op_library(library_filename) 35 self.assertEqual(tf.add(1, 41).eval(), 42) 39 tf.test.main()
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/external/clang/test/PCH/ |
H A D | headersearch.cpp | 10 // RUN: echo 'template <typename T> void tf() { orig_sub2_1(); T::foo(); }' >> %t_orig/sub2/orig_sub2.h 44 tf<int>();
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H A D | missing-file.cpp | 5 // RUN: echo 'template <typename T> void tf() { T::foo(); }' >> %t.h 24 tf<int>();
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/external/tensorflow/tensorflow/examples/adding_an_op/ |
H A D | cuda_op.py | 22 import tensorflow as tf namespace 24 if tf.test.is_built_with_cuda(): 25 _cuda_op_module = tf.load_op_library(os.path.join( 26 tf.resource_loader.get_data_files_path(), 'cuda_op_kernel.so'))
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H A D | fact_test.py | 21 import tensorflow as tf namespace 24 class FactTest(tf.test.TestCase): 28 print(tf.user_ops.my_fact().eval()) 32 tf.test.main()
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/external/tensorflow/tensorflow/examples/benchmark/ |
H A D | sample_benchmark.py | 22 import tensorflow as tf namespace 25 # Define a class that extends from tf.test.Benchmark. 26 class SampleBenchmark(tf.test.Benchmark): 30 with tf.Session() as sess: 31 x = tf.constant(10) 32 y = tf.constant(5) 33 result = tf.add(x, y) 50 tf.test.main()
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/external/tensorflow/tensorflow/python/framework/ |
H A D | contrib_test.py | 29 import tensorflow as tf namespace 30 _ = tf.contrib.layers # `tf.contrib` is loaded lazily on first use. 31 assert tf_inspect.ismodule(tf.contrib) 35 import tensorflow as tf namespace 36 assert tf_inspect.ismodule(tf.contrib.layers) 40 import tensorflow as tf namespace 41 assert tf_inspect.ismodule(tf.contrib.linear_optimizer)
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/external/tensorflow/tensorflow/contrib/graph_editor/examples/ |
H A D | edit_graph_example.py | 24 import tensorflow as tf namespace 27 FLAGS = tf.flags.FLAGS 32 g = tf.Graph() 34 a = tf.constant(1.0, shape=[2, 3], name="a") 35 b = tf.constant(2.0, shape=[2, 3], name="b") 36 c = tf.add( 37 tf.placeholder(dtype=np.float32), 38 tf.placeholder(dtype=np.float32), 48 with tf.Session(graph=g) as sess: 54 tf [all...] |
/external/curl/packages/vms/ |
H A D | config_h.com | 171 $ write tf "" 172 $ write tf - 174 $ write tf - 176 $ write tf - 180 $ write tf - 183 $ write tf - 185 $ write tf "" 256 $ write tf "" 267 $ write tf line_in 293 $ write tf "/* ", xlin [all...] |
/external/tensorflow/tensorflow/examples/speech_commands/ |
H A D | models.py | 24 import tensorflow as tf namespace 122 saver = tf.train.Saver(tf.global_variables()) 152 dropout_prob = tf.placeholder(tf.float32, name='dropout_prob') 155 weights = tf.Variable( 156 tf.truncated_normal([fingerprint_size, label_count], stddev=0.001)) 157 bias = tf.Variable(tf.zeros([label_count])) 158 logits = tf [all...] |
/external/tensorflow/tensorflow/tools/compatibility/testdata/ |
H A D | test_file_v0_11.py | 15 """Tests for tf upgrader.""" 23 import tensorflow as tf namespace 46 tf.reduce_any( 49 tf.reduce_all( 52 tf.reduce_all( 55 tf.reduce_sum( 58 tf.reduce_sum( 60 self.assertAllEqual(tf.reduce_sum(a, [0, 1]).eval(), 21.0) 62 tf.reduce_prod( 65 tf [all...] |
/external/tensorflow/tensorflow/examples/saved_model/ |
H A D | saved_model_half_plus_two.py | 46 import tensorflow as tf namespace 68 tf.compat.as_bytes(assets_directory), tf.compat.as_bytes(assets_filename)) 75 input_tensor_info = tf.saved_model.utils.build_tensor_info(input_tensor) 77 tf.saved_model.signature_constants.REGRESS_INPUTS: input_tensor_info 79 output_tensor_info = tf.saved_model.utils.build_tensor_info(output_tensor) 81 tf.saved_model.signature_constants.REGRESS_OUTPUTS: output_tensor_info 83 return tf.saved_model.signature_def_utils.build_signature_def( 85 tf.saved_model.signature_constants.REGRESS_METHOD_NAME) 92 input_tensor_info = tf [all...] |
/external/tensorflow/tensorflow/examples/learn/ |
H A D | mnist.py | 25 import tensorflow as tf namespace 36 feature = tf.reshape(features[X_FEATURE], [-1, 28, 28, 1]) 39 with tf.variable_scope('conv_layer1'): 40 h_conv1 = tf.layers.conv2d( 45 activation=tf.nn.relu) 46 h_pool1 = tf.layers.max_pooling2d( 50 with tf.variable_scope('conv_layer2'): 51 h_conv2 = tf.layers.conv2d( 56 activation=tf.nn.relu) 57 h_pool2 = tf [all...] |
/external/tensorflow/tensorflow/python/debug/examples/ |
H A D | debug_mnist.py | 30 import tensorflow as tf namespace 57 sess = tf.InteractiveSession() 62 with tf.name_scope("input"): 63 x = tf.placeholder( 64 tf.float32, [None, IMAGE_SIZE * IMAGE_SIZE], name="x-input") 65 y_ = tf.placeholder(tf.float32, [None, NUM_LABELS], name="y-input") 69 initial = tf.truncated_normal(shape, stddev=0.1, seed=RAND_SEED) 70 return tf.Variable(initial) 74 initial = tf [all...] |
/external/tensorflow/tensorflow/contrib/eager/python/examples/rnn_colorbot/ |
H A D | rnn_colorbot_test.py | 20 import tensorflow as tf namespace 37 chars = tf.one_hot( 38 tf.random_uniform( 39 [batch_size, time_steps], minval=0, maxval=alphabet, dtype=tf.int32), 41 sequence_length = tf.constant( 42 [time_steps for _ in range(batch_size)], dtype=tf.int64) 43 labels = tf.random_normal([batch_size, LABEL_DIMENSION]) 44 return tf.data.Dataset.from_tensors((labels, chars, sequence_length)) 47 class RNNColorbotTest(tf.test.TestCase): 54 optimizer = tf [all...] |
/external/tensorflow/tensorflow/examples/tutorials/layers/ |
H A D | cnn_mnist.py | 14 """Convolutional Neural Network Estimator for MNIST, built with tf.layers.""" 21 import tensorflow as tf namespace 23 tf.logging.set_verbosity(tf.logging.INFO) 31 input_layer = tf.reshape(features["x"], [-1, 28, 28, 1]) 38 conv1 = tf.layers.conv2d( 43 activation=tf.nn.relu) 49 pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2) 56 conv2 = tf.layers.conv2d( 61 activation=tf [all...] |
/external/tensorflow/tensorflow/contrib/session_bundle/example/ |
H A D | export_half_plus_two.py | 35 import tensorflow as tf namespace 43 with tf.Session() as sess: 46 a = tf.Variable(0.5, name="a") 47 b = tf.Variable(2.0, name="b") 50 serialized_tf_example = tf.placeholder(tf.string, name="tf_example") 54 feature_configs = {"x": tf.FixedLenFeature([1], dtype=tf.float32),} 55 tf_example = tf.parse_example(serialized_tf_example, feature_configs) 56 # Use tf [all...] |