/external/tensorflow/tensorflow/contrib/gan/python/eval/python/ |
H A D | eval_utils_impl.py | 34 def image_grid(input_tensor, grid_shape, image_shape=(32, 32), num_channels=3): 38 input_tensor: Tensor. Minibatch of images to format, either 4D 55 if grid_shape[0] * grid_shape[1] != int(input_tensor.shape[0]): 57 (grid_shape, int(input_tensor.shape[0]))) 58 if len(input_tensor.shape) == 2: 60 if int(input_tensor.shape[1]) != num_features: 63 elif len(input_tensor.shape) == 4: 64 if (int(input_tensor.shape[1]) != image_shape[0] or 65 int(input_tensor.shape[2]) != image_shape[1] or 66 int(input_tensor [all...] |
/external/tensorflow/tensorflow/contrib/quantize/python/ |
H A D | input_to_ops_test.py | 34 input_tensor = array_ops.zeros((1, 2, 3, 4)) 37 consumer_operations = input_to_ops_map.ConsumerOperations(input_tensor.op) 44 input_tensor = array_ops.zeros((1, 2, 3, 4)) 45 output_tensor = nn_ops.relu6(input_tensor) 48 consumer_operations = input_to_ops_map.ConsumerOperations(input_tensor.op) 55 input_tensor = array_ops.zeros((1, 2, 3, 4)) 56 output_tensor_1 = nn_ops.relu6(input_tensor) 57 output_tensor_2 = input_tensor + output_tensor_1 58 output_tensor_3 = input_tensor * output_tensor_2 61 consumer_operations = input_to_ops_map.ConsumerOperations(input_tensor [all...] |
/external/tensorflow/tensorflow/core/kernels/ |
H A D | bitcast_op.cc | 42 const Tensor& input_tensor = context->input(0); variable 44 TensorShape adjusted_shape = input_tensor.shape(); 47 (input_tensor.dims() > 0 && 48 input_tensor.dim_size(input_tensor.dims() - 1) == 50 input_tensor.dim_size(input_tensor.dims()) == -1, 54 input_tensor.shape().DebugString())); 59 adjusted_shape.RemoveDim(input_tensor.dims() - 1); 63 output_tensor.UnsafeCopyFromInternal(input_tensor, output_data_type [all...] |
H A D | guarantee_const_op.cc | 32 const Tensor& input_tensor = ctx->input(0); variable 34 if (!ctx->forward_input_to_output_with_shape(0, 0, input_tensor.shape(), 36 ctx->set_output(0, input_tensor);
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H A D | nth_element_op.h | 29 void operator()(OpKernelContext* context, const Tensor& input_tensor,
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H A D | string_to_number_op.cc | 41 const Tensor* input_tensor; variable 42 OP_REQUIRES_OK(context, context->input("string_tensor", &input_tensor)); 43 const auto& input_flat = input_tensor->flat<string>(); 47 context->allocate_output("output", input_tensor->shape(),
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H A D | base64_ops.cc | 34 const Tensor& input_tensor = context->input(0); variable 36 OP_REQUIRES_OK(context, context->allocate_output(0, input_tensor.shape(), 39 auto input = input_tensor.flat<string>(); 59 const Tensor& input_tensor = context->input(0); variable 61 OP_REQUIRES_OK(context, context->allocate_output(0, input_tensor.shape(), 64 auto input = input_tensor.flat<string>();
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H A D | string_to_hash_bucket_op.h | 37 const Tensor* input_tensor; variable 38 OP_REQUIRES_OK(context, context->input("input", &input_tensor)); 39 const auto& input_flat = input_tensor->flat<string>(); 43 context->allocate_output("output", input_tensor->shape(), 79 const Tensor* input_tensor; variable 80 OP_REQUIRES_OK(context, context->input("input", &input_tensor)); 81 const auto& input_flat = input_tensor->flat<string>(); 85 context->allocate_output("output", input_tensor->shape(),
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H A D | string_to_hash_bucket_op.cc | 34 const Tensor* input_tensor; variable 35 OP_REQUIRES_OK(context, context->input("string_tensor", &input_tensor)); 36 const auto& input_flat = input_tensor->flat<string>(); 40 context->allocate_output("output", input_tensor->shape(),
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/external/tensorflow/tensorflow/contrib/periodic_resample/python/kernel_tests/ |
H A D | periodic_resample_op_test.py | 34 input_tensor = numpy.arange(12).reshape((3, 4)) 36 output_tensor = input_tensor.reshape((6, 2)) 40 result = periodic_resample(input_tensor, desired_shape).eval() 45 input_tensor = numpy.arange(12).reshape((3, 4)) 47 output_tensor = input_tensor.reshape((6, 2))[:-1] 51 result = periodic_resample(input_tensor, desired_shape).eval() 56 input_tensor = numpy.arange(2 * 2 * 4).reshape((2, 2, 4)) 62 # NOTE: output_tensor != input_tensor.reshape((4, 4, -1)) 65 result = periodic_resample(input_tensor, desired_shape).eval() 66 # input_tensor[ [all...] |
/external/tensorflow/tensorflow/python/ops/ |
H A D | spectral_ops.py | 47 def _infer_fft_length_for_rfft(input_tensor, fft_rank): 48 """Infers the `fft_length` argument for a `rank` RFFT from `input_tensor`.""" 50 fft_shape = input_tensor.get_shape()[-fft_rank:] 54 return _array_ops.shape(input_tensor)[-fft_rank:] 60 def _infer_fft_length_for_irfft(input_tensor, fft_rank): 61 """Infers the `fft_length` argument for a `rank` IRFFT from `input_tensor`.""" 63 fft_shape = input_tensor.get_shape()[-fft_rank:] 67 fft_length = _array_ops.unstack(_array_ops.shape(input_tensor)[-fft_rank:]) 78 def _maybe_pad_for_rfft(input_tensor, fft_rank, fft_length, is_reverse=False): 79 """Pads `input_tensor` t [all...] |
/external/tensorflow/tensorflow/contrib/layers/python/ops/ |
H A D | bucketization_op.py | 23 def bucketize(input_tensor, boundaries, name=None): 24 """Bucketizes input_tensor by given boundaries. 29 input_tensor: A `Tensor` which will be bucketize. 35 each value in `input_tensor`. 41 input_tensor, boundaries=boundaries, name=name)
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/external/tensorflow/tensorflow/core/kernels/fuzzing/ |
H A D | identity_fuzz.cc | 26 Tensor input_tensor(tensorflow::DT_INT8, 28 auto flat_tensor = input_tensor.flat<int8>(); 33 Status s = RunOneInput(input_tensor);
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H A D | parse_tensor_op_fuzz.cc | 34 Tensor input_tensor(tensorflow::DT_STRING, TensorShape({})); 35 input_tensor.scalar<string>()() = 38 RunOneInput(input_tensor).IgnoreError();
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H A D | encode_jpeg_fuzz.cc | 49 Tensor input_tensor(tensorflow::DT_UINT8, 51 auto flat_tensor = input_tensor.flat<uint8>(); 56 RunOneInput(input_tensor).IgnoreError();
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H A D | string_split_fuzz.cc | 33 Tensor input_tensor(tensorflow::DT_STRING, TensorShape({})); 48 input_tensor.scalar<string>()() = string( 53 RunTwoInputs(input_tensor, delimeter_tensor).IgnoreError();
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H A D | fuzz_session.h | 65 // RunOneInput(input_tensor); 111 // the supplied input_tensor to the "input1" node, and discarding 113 Status RunOneInput(const Tensor& input_tensor) { argument 114 return session_->Run({{"input1", input_tensor}}, {}, {"output"}, nullptr); 145 Tensor input_tensor(tensorflow::DT_STRING, TensorShape({})); 146 input_tensor.scalar<string>()() = 149 RunOneInput(input_tensor).IgnoreError();
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/external/tensorflow/tensorflow/examples/adding_an_op/ |
H A D | cuda_op_kernel.cc | 39 const Tensor& input_tensor = context->input(0); variable 40 auto input = input_tensor.flat<int32>(); 44 OP_REQUIRES_OK(context, context->allocate_output(0, input_tensor.shape(),
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H A D | zero_out_op_kernel_1.cc | 42 const Tensor& input_tensor = context->input(0); variable 43 auto input = input_tensor.flat<int32>(); 47 OP_REQUIRES_OK(context, context->allocate_output(0, input_tensor.shape(),
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/external/tensorflow/tensorflow/tools/ci_build/builds/user_ops/ |
H A D | cuda_op_kernel.cc | 39 const Tensor& input_tensor = context->input(0); variable 40 auto input = input_tensor.flat<int32>(); 44 OP_REQUIRES_OK(context, context->allocate_output(0, input_tensor.shape(),
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H A D | zero_out_op_kernel_1.cc | 42 const Tensor& input_tensor = context->input(0); variable 43 auto input = input_tensor.flat<int32>(); 47 OP_REQUIRES_OK(context, context->allocate_output(0, input_tensor.shape(),
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/external/tensorflow/tensorflow/contrib/text/python/ops/ |
H A D | skip_gram_ops.py | 41 def skip_gram_sample(input_tensor, 58 rank-1 `input_tensor` as a token. The window size used for each token will be 63 For example, given `input_tensor = ["the", "quick", "brown", "fox", "jumps"]`, 76 The same process is repeated for each element of `input_tensor` and 80 If `vocab_freq_table` is specified, tokens in `input_tensor` that are not 91 input_tensor: A rank-1 `Tensor` from which to generate skip-gram candidates. 100 `input_tensor` from which to start generating skip-gram candidates. 102 elements in `input_tensor` to use in generating skip-gram candidates. -1 108 frequency counts. If specified, any token in `input_tensor` that is not 115 kept in `input_tensor` [all...] |
H A D | skip_gram_ops_test.py | 46 input_tensor = constant_op.constant( 49 input_tensor, min_skips=2, max_skips=2) 72 input_tensor = constant_op.constant( 75 input_tensor, min_skips=2, max_skips=2, emit_self_as_target=True) 103 input_tensor = constant_op.constant([b"the", b"quick", b"brown"]) 107 input_tensor, min_skips=0, max_skips=0, emit_self_as_target=False) 114 input_tensor, min_skips=0, max_skips=0, emit_self_as_target=True) 126 input_tensor = constant_op.constant([b"the", b"quick", b"brown"]) 128 input_tensor, min_skips=100, max_skips=100) 143 input_tensor [all...] |
/external/tensorflow/tensorflow/contrib/layers/python/layers/ |
H A D | feature_column.py | 166 ["input_tensor", 180 ["input_tensor", 251 input_tensor, 258 def _deep_embedding_lookup_arguments(self, input_tensor): 266 def _wide_embedding_lookup_arguments(self, input_tensor): 272 def _to_dense_tensor(self, input_tensor): 414 def id_tensor(self, input_tensor): 415 """Returns the id tensor from the given transformed input_tensor.""" 416 return input_tensor 419 def weight_tensor(self, input_tensor) [all...] |
/external/tensorflow/tensorflow/contrib/tensor_forest/kernels/ |
H A D | scatter_add_ndim_op.cc | 34 Tensor input_tensor = context->mutable_input(0, false); variable 45 input_tensor.shape().dims() + 1, 60 if (!CheckTensorBounds(context, input_tensor)) return; 64 auto input = input_tensor.flat<float>(); 75 for (int32 i = 0; i < input_tensor.shape().dims() - num_dims; ++i) { 76 num_data_per_index *= input_tensor.shape().dim_size(num_dims + i); 87 const int32 m = last_size / input_tensor.shape().dim_size(j);
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