/external/tensorflow/tensorflow/contrib/slim/python/slim/nets/ |
inception_v2.py | 84 layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d, 121 net = layers.conv2d( 131 net = layers.conv2d(net, depth(192), [3, 3], scope=end_point) 146 branch_0 = layers.conv2d( 149 branch_1 = layers.conv2d( 154 branch_1 = layers.conv2d( 157 branch_2 = layers.conv2d( 162 branch_2 = layers.conv2d( 164 branch_2 = layers.conv2d( 168 branch_3 = layers.conv2d( [all...] |
inception_v3.py | 107 [layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d], 112 net = layers.conv2d(inputs, depth(32), [3, 3], stride=2, scope=end_point) 118 net = layers.conv2d(net, depth(32), [3, 3], scope=end_point) 124 net = layers.conv2d( 137 net = layers.conv2d(net, depth(80), [1, 1], scope=end_point) 143 net = layers.conv2d(net, depth(192), [3, 3], scope=end_point) 157 [layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d], 164 branch_0 = layers.conv2d( 167 branch_1 = layers.conv2d( 169 branch_1 = layers.conv2d( [all...] |
inception_v1.py | 62 [layers.conv2d, layers_lib.fully_connected], 65 [layers.conv2d, layers_lib.max_pool2d], stride=1, padding='SAME'): 67 net = layers.conv2d(inputs, 64, [7, 7], stride=2, scope=end_point) 77 net = layers.conv2d(net, 64, [1, 1], scope=end_point) 82 net = layers.conv2d(net, 192, [3, 3], scope=end_point) 95 branch_0 = layers.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1') 97 branch_1 = layers.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1') 98 branch_1 = layers.conv2d( 101 branch_2 = layers.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1') 102 branch_2 = layers.conv2d( [all...] |
vgg.py | 66 [layers.conv2d, layers_lib.fully_connected], 70 with arg_scope([layers.conv2d], padding='SAME') as arg_sc: 82 Note: All the fully_connected layers have been transformed to conv2d layers. 100 # Collect outputs for conv2d, fully_connected and max_pool2d. 102 [layers.conv2d, layers_lib.max_pool2d], 105 inputs, 1, layers.conv2d, 64, [3, 3], scope='conv1') 107 net = layers_lib.repeat(net, 1, layers.conv2d, 128, [3, 3], scope='conv2') 109 net = layers_lib.repeat(net, 2, layers.conv2d, 256, [3, 3], scope='conv3') 111 net = layers_lib.repeat(net, 2, layers.conv2d, 512, [3, 3], scope='conv4') 113 net = layers_lib.repeat(net, 2, layers.conv2d, 512, [3, 3], scope='conv5' [all...] |
alexnet.py | 54 [layers.conv2d, layers_lib.fully_connected], 58 with arg_scope([layers.conv2d], padding='SAME'): 76 Note: All the fully_connected layers have been transformed to conv2d layers. 97 # Collect outputs for conv2d, fully_connected and max_pool2d. 99 [layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d], 101 net = layers.conv2d( 104 net = layers.conv2d(net, 192, [5, 5], scope='conv2') 106 net = layers.conv2d(net, 384, [3, 3], scope='conv3') 107 net = layers.conv2d(net, 384, [3, 3], scope='conv4') 108 net = layers.conv2d(net, 256, [3, 3], scope='conv5' [all...] |
overfeat.py | 50 [layers.conv2d, layers_lib.fully_connected], 54 with arg_scope([layers.conv2d], padding='SAME'): 74 Note: All the fully_connected layers have been transformed to conv2d layers. 94 # Collect outputs for conv2d, fully_connected and max_pool2d 96 [layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d], 98 net = layers.conv2d( 101 net = layers.conv2d(net, 256, [5, 5], padding='VALID', scope='conv2') 103 net = layers.conv2d(net, 512, [3, 3], scope='conv3') 104 net = layers.conv2d(net, 1024, [3, 3], scope='conv4') 105 net = layers.conv2d(net, 1024, [3, 3], scope='conv5' [all...] |
resnet_v2.py | 105 shortcut = layers_lib.conv2d( 113 residual = layers_lib.conv2d( 117 residual = layers_lib.conv2d( 200 [layers_lib.conv2d, bottleneck, resnet_utils.stack_blocks_dense], 213 [layers_lib.conv2d], activation_fn=None, normalizer_fn=None): 226 net = layers_lib.conv2d(
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resnet_v1.py | 109 shortcut = layers.conv2d( 116 residual = layers.conv2d( 120 residual = layers.conv2d( 196 [layers.conv2d, bottleneck, resnet_utils.stack_blocks_dense], 212 net = layers.conv2d(
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resnet_utils.py | 90 When stride > 1, then we do explicit zero-padding, followed by conv2d with 99 net = tf.contrib.layers.conv2d(inputs, num_outputs, 3, stride=1, 105 net = tf.contrib.layers.conv2d(inputs, num_outputs, 3, stride=stride, 124 return layers_lib.conv2d( 139 return layers_lib.conv2d( 256 [layers_lib.conv2d],
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/external/tensorflow/tensorflow/contrib/receptive_field/python/util/ |
receptive_field_test.py | 51 l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='VALID') 54 l2 = slim.conv2d(l2_pad, 1, [3, 3], stride=2, scope='L2', padding='VALID') 55 l3 = slim.conv2d(l2, 1, [1, 1], stride=2, scope='L3', padding='VALID') 79 l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='VALID') 105 l1 = slim.conv2d(l1_pad, 1, [5, 5], stride=2, scope='L1', padding='VALID') 107 l2 = slim.conv2d(x, 1, [3, 3], stride=1, scope='L2', padding='VALID') 108 l3 = slim.conv2d(l2, 1, [3, 3], stride=1, scope='L3', padding='VALID') 132 l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='VALID') 134 l2 = slim.conv2d(x, 1, [3, 3], stride=2, scope='L2', padding='SAME') 135 l3 = slim.conv2d(l2, 1, [1, 1], stride=2, scope='L3', padding='VALID' [all...] |
graph_compute_order_test.py | 43 l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='VALID') 46 l2 = slim.conv2d(l2_pad, 1, [3, 3], stride=2, scope='L2', padding='VALID') 51 l5 = slim.conv2d(l4, 1, [1, 1], stride=2, scope='L5', padding='SAME') 53 l6 = slim.conv2d(l4, 1, [3, 3], stride=2, scope='L6', padding='SAME') 127 'L1/Conv2D': [224, 224], 129 'L2/Conv2D': [225, 225], 132 'L5/Conv2D': [56, 56], 133 'L6/Conv2D': [56, 56], 138 'L1/Conv2D': [56, 56], 140 'L2/Conv2D': [112, 112] [all...] |
parse_layer_parameters_test.py | 45 l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='VALID') 48 l2 = slim.conv2d(l2_pad, 1, [3, 3], stride=2, scope='L2', padding='VALID') 53 l5 = slim.conv2d(l4, 1, [1, 1], stride=2, scope='L5', padding='SAME') 55 l6 = slim.conv2d(l4, 1, [3, 3], stride=2, scope='L6', padding='SAME') 70 l1_node_name = 'L1/Conv2D' 84 l2_node_name = 'L2/Conv2D' 116 l5_node_name = 'L5/Conv2D' 123 l6_node_name = 'L6/Conv2D'
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/external/tensorflow/tensorflow/examples/tutorials/mnist/ |
mnist_deep.py | 63 h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) 73 h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) 103 def conv2d(x, W): function 104 """conv2d returns a 2d convolution layer with full stride.""" 105 return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
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/external/tensorflow/tensorflow/python/ops/ |
conv2d_benchmark.py | 15 """Benchmark for Conv2D op.""" 36 """builds a graph containing a sequence of conv2d operations. 47 num_iters: number of iterations to run conv2d. 60 conv2d_op = nn_ops.conv2d(inp, filt, strides, padding, data_format="NHWC") 64 conv2d_op = nn_ops.conv2d( 69 warmup_conv2d_op = nn_ops.conv2d( 74 warmup_conv2d_op = nn_ops.conv2d( 82 """Benchmark conv2d!""" 98 num_iters: number of iterations to run conv2d. 141 print("conv2d benchmark:" [all...] |
/external/tensorflow/tensorflow/python/profiler/internal/ |
model_analyzer_testlib.py | 49 x = nn_ops.conv2d(image, kernel, [1, 2, 2, 1], padding='SAME') 54 x = nn_ops.conv2d(x, kernel, [1, 2, 2, 1], padding='SAME') 83 r1 = nn_ops.conv2d(image, kernel1, [1, 2, 2, 1], padding='SAME') 89 r2 = nn_ops.conv2d(image, kernel2, [1, 2, 2, 1], padding='SAME')
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print_model_analysis_test.py | 60 x = nn_ops.conv2d(image, kernel, [1, 2, 2, 1], padding='SAME')
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/external/tensorflow/tensorflow/python/layers/ |
maxout_test.py | 50 graph = conv_layers.conv2d(inputs, 10, 3, padding="SAME") 56 graph = conv_layers.conv2d(inputs, 3, 10, strides=(1, 1))
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/external/tensorflow/tensorflow/contrib/specs/python/ |
specs_ops.py | 80 Cx = Fun(layers.conv2d) 81 Cs = Fun(layers.conv2d, activation_fn=math_ops.sigmoid) 82 Ct = Fun(layers.conv2d, activation_fn=math_ops.tanh) 83 Cr = Fun(layers.conv2d, activation_fn=nn_ops.relu) 84 Cm = Fun(layers.conv2d, activation_fn=nn_ops.softmax) 85 Cl = Fun(layers.conv2d, activation_fn=None)
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/external/tensorflow/tensorflow/examples/learn/ |
resnet.py | 58 net = tf.layers.conv2d( 71 net = tf.layers.conv2d( 85 conv = tf.layers.conv2d( 94 conv = tf.layers.conv2d( 105 conv = tf.layers.conv2d( 121 net = tf.layers.conv2d(
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mnist.py | 40 h_conv1 = tf.layers.conv2d( 51 h_conv2 = tf.layers.conv2d(
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text_classification_cnn.py | 53 conv1 = tf.layers.conv2d( 70 conv2 = tf.layers.conv2d(
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/external/tensorflow/tensorflow/python/kernel_tests/ |
conv2d_backprop_filter_grad_test.py | 45 conv_out = nn_ops.conv2d( 79 conv_out = nn_ops.conv2d(
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atrous_conv2d_test.py | 80 y2 = nn_ops.conv2d( 99 net = conv2d(net, filters1, strides=[1, 1, 1, 1], padding="SAME") 100 net = conv2d(net, filters2, strides=[1, 1, 1, 1], padding="SAME") 102 net = conv2d(net, filtersK, strides=[1, 1, 1, 1], padding="SAME") 125 # y2: space_to_batch, three conv2d in a row, batch_to_space 130 y2 = nn_ops.conv2d(y2, f, strides=[1, 1, 1, 1], padding=padding) 131 y2 = nn_ops.conv2d(y2, f, strides=[1, 1, 1, 1], padding=padding) 132 y2 = nn_ops.conv2d(y2, f, strides=[1, 1, 1, 1], padding=padding)
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/external/tensorflow/tensorflow/contrib/quantize/python/ |
quantize_test.py | 32 conv2d = layers.conv2d variable 51 conv = conv2d(inputs, 32, [5, 5], stride=2, padding='SAME', 73 conv = conv2d(input1, 32, [5, 5], stride=2, padding='SAME',
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/external/tensorflow/tensorflow/examples/tutorials/layers/ |
cnn_mnist.py | 38 conv1 = tf.layers.conv2d( 56 conv2 = tf.layers.conv2d(
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