diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc index bbdcfd337e2..e528ae47aa7 100644 --- a/tensorflow/core/ops/array_ops.cc +++ b/tensorflow/core/ops/array_ops.cc @@ -209,7 +209,7 @@ The input tensors are all required to have size 1 in the first dimension. For example: -```prettyprint +``` # 'x' is [[1, 4]] # 'y' is [[2, 5]] # 'z' is [[3, 6]] @@ -277,7 +277,7 @@ Etc. For example: -```prettyprint +``` # 'x' is [1, 4] # 'y' is [2, 5] # 'z' is [3, 6] @@ -432,7 +432,7 @@ Computes offsets of concat inputs within its output. For example: -```prettyprint +``` # 'x' is [2, 2, 7] # 'y' is [2, 3, 7] # 'z' is [2, 5, 7] @@ -670,7 +670,7 @@ rank 2k with dimensions [D1,..., Dk, D1,..., Dk] where: For example: -```prettyprint +``` # 'diagonal' is [1, 2, 3, 4] tf.diag(diagonal) ==> [[1, 0, 0, 0] [0, 2, 0, 0] @@ -722,7 +722,7 @@ tensor of rank `k` with dimensions `[D1,..., Dk]` where: For example: -```prettyprint +``` # 'input' is [[1, 0, 0, 0] [0, 2, 0, 0] [0, 0, 3, 0] @@ -768,7 +768,7 @@ tensor of rank `k+1` with dimensions [I, J, K, ..., N, N]` where: For example: -```prettyprint +``` # 'diagonal' is [[1, 2, 3, 4], [5, 6, 7, 8]] and diagonal.shape = (2, 4) @@ -880,7 +880,7 @@ The input must be at least a matrix. For example: -```prettyprint +``` # 'input' is [[[1, 0, 0, 0] [0, 2, 0, 0] [0, 0, 3, 0] @@ -927,7 +927,7 @@ The indicator function For example: -```prettyprint +``` # if 'input' is [[ 0, 1, 2, 3] [-1, 0, 1, 2] [-2, -1, 0, 1] @@ -946,7 +946,7 @@ tf.matrix_band_part(input, 2, 1) ==> [[ 0, 1, 0, 0] Useful special cases: -```prettyprint +``` tf.matrix_band_part(input, 0, -1) ==> Upper triangular part. tf.matrix_band_part(input, -1, 0) ==> Lower triangular part. tf.matrix_band_part(input, 0, 0) ==> Diagonal. @@ -998,7 +998,7 @@ of `tensor` must equal the number of elements in `dims`. In other words: For example: -```prettyprint +``` # tensor 't' is [[[[ 0, 1, 2, 3], # [ 4, 5, 6, 7], # [ 8, 9, 10, 11]], @@ -1074,7 +1074,7 @@ once, a InvalidArgument error is raised. For example: -```prettyprint +``` # tensor 't' is [[[[ 0, 1, 2, 3], # [ 4, 5, 6, 7], # [ 8, 9, 10, 11]], @@ -1245,7 +1245,7 @@ This operation creates a tensor of shape `dims` and fills it with `value`. For example: -```prettyprint +``` # Output tensor has shape [2, 3]. fill([2, 3], 9) ==> [[9, 9, 9] [9, 9, 9]] @@ -1354,7 +1354,7 @@ out-of-bound indices result in safe but unspecified behavior, which may include raising an error.
- +
)doc"); @@ -1610,7 +1610,7 @@ implied by `shape` must be the same as the number of elements in `tensor`. For example: -```prettyprint +``` # tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9] # tensor 't' has shape [9] reshape(t, [3, 3]) ==> [[1, 2, 3], @@ -1697,7 +1697,7 @@ The values must include 0. There can be no duplicate values or negative values. For example: -```prettyprint +``` # tensor `x` is [3, 4, 0, 2, 1] invert_permutation(x) ==> [2, 4, 3, 0, 1] ``` @@ -1802,7 +1802,7 @@ in the unique output `y`. In other words: For example: -```prettyprint +``` # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8] y, idx = unique(x) y ==> [1, 2, 4, 7, 8] @@ -1842,7 +1842,7 @@ contains the count of each element of `y` in `x`. In other words: For example: -```prettyprint +``` # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8] y, idx, count = unique_with_counts(x) y ==> [1, 2, 4, 7, 8] @@ -1887,7 +1887,7 @@ This operation returns a 1-D integer tensor representing the shape of `input`. For example: -```prettyprint +``` # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] shape(t) ==> [2, 2, 3] ``` @@ -1968,7 +1968,7 @@ slice `i`, with the first `seq_lengths[i]` slices along dimension For example: -```prettyprint +``` # Given this: batch_dim = 0 seq_dim = 1 @@ -1990,7 +1990,7 @@ output[3, 2:, :, ...] = input[3, 2:, :, ...] In contrast, if: -```prettyprint +``` # Given this: batch_dim = 2 seq_dim = 0 @@ -2031,7 +2031,7 @@ This operation returns an integer representing the rank of `input`. For example: -```prettyprint +``` # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] # shape of tensor 't' is [2, 2, 3] rank(t) ==> 3 @@ -2057,7 +2057,7 @@ This operation returns an integer representing the number of elements in For example: -```prettyprint +``` # 't' is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]] size(t) ==> 12 ``` @@ -2290,7 +2290,7 @@ encoding is best understand by considering a non-trivial example. In particular, `foo[1, 2:4, None, ..., :-3:-1, :]` will be encoded as -```prettyprint +``` begin = [1, 2, x, x, 0, x] # x denotes don't care (usually 0) end = [2, 4, x, x, -3, x] strides = [1, 1, x, x, -1, 1] @@ -2512,7 +2512,7 @@ the output tensor can vary depending on how many true values there are in For example: -```prettyprint +``` # 'input' tensor is [[True, False] # [True, False]] # 'input' has two true values, so output has two coordinates. @@ -2616,7 +2616,7 @@ The padded size of each dimension D of the output is: For example: -```prettyprint +``` # 't' is [[1, 1], [2, 2]] # 'paddings' is [[1, 1], [2, 2]] # rank of 't' is 2 @@ -2655,7 +2655,7 @@ The padded size of each dimension D of the output is: For example: -```prettyprint +``` # 't' is [[1, 2, 3], [4, 5, 6]]. # 'paddings' is [[1, 1]], [2, 2]]. # 'mode' is SYMMETRIC. @@ -2751,7 +2751,7 @@ The folded size of each dimension D of the output is: For example: -```prettyprint +``` # 't' is [[1, 2, 3], [4, 5, 6], [7, 8, 9]]. # 'paddings' is [[0, 1]], [0, 1]]. # 'mode' is SYMMETRIC. @@ -2927,7 +2927,7 @@ which will make the shape `[1, height, width, channels]`. Other examples: -```prettyprint +``` # 't' is a tensor of shape [2] shape(expand_dims(t, 0)) ==> [1, 2] shape(expand_dims(t, 1)) ==> [2, 1] @@ -3029,14 +3029,14 @@ dimensions, you can remove specific size 1 dimensions by specifying For example: -```prettyprint +``` # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] shape(squeeze(t)) ==> [2, 3] ``` Or, to remove specific size 1 dimensions: -```prettyprint +``` # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1] ``` @@ -3079,14 +3079,14 @@ position of each `out` element in `x`. In other words: For example, given this input: -```prettyprint +``` x = [1, 2, 3, 4, 5, 6] y = [1, 3, 5] ``` This operation would return: -```prettyprint +``` out ==> [2, 4, 6] idx ==> [1, 3, 5] ``` @@ -3345,34 +3345,34 @@ Some examples: (1) For the following input of shape `[1, 2, 2, 1]`, `block_shape = [2, 2]`, and `paddings = [[0, 0], [0, 0]]`: -```prettyprint +``` x = [[[[1], [2]], [[3], [4]]]] ``` The output tensor has shape `[4, 1, 1, 1]` and value: -```prettyprint +``` [[[[1]]], [[[2]]], [[[3]]], [[[4]]]] ``` (2) For the following input of shape `[1, 2, 2, 3]`, `block_shape = [2, 2]`, and `paddings = [[0, 0], [0, 0]]`: -```prettyprint +``` x = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]] ``` The output tensor has shape `[4, 1, 1, 3]` and value: -```prettyprint +``` [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]] ``` (3) For the following input of shape `[1, 4, 4, 1]`, `block_shape = [2, 2]`, and `paddings = [[0, 0], [0, 0]]`: -```prettyprint +``` x = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]], [[9], [10], [11], [12]], @@ -3381,7 +3381,7 @@ x = [[[[1], [2], [3], [4]], The output tensor has shape `[4, 2, 2, 1]` and value: -```prettyprint +``` x = [[[[1], [3]], [[9], [11]]], [[[2], [4]], [[10], [12]]], [[[5], [7]], [[13], [15]]], @@ -3391,7 +3391,7 @@ x = [[[[1], [3]], [[9], [11]]], (4) For the following input of shape `[2, 2, 4, 1]`, block_shape = `[2, 2]`, and paddings = `[[0, 0], [2, 0]]`: -```prettyprint +``` x = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]]], [[[9], [10], [11], [12]], @@ -3400,7 +3400,7 @@ x = [[[[1], [2], [3], [4]], The output tensor has shape `[8, 1, 3, 1]` and value: -```prettyprint +``` x = [[[[0], [1], [3]]], [[[0], [9], [11]]], [[[0], [2], [4]]], [[[0], [10], [12]]], [[[0], [5], [7]]], [[[0], [13], [15]]], @@ -3474,32 +3474,32 @@ Some examples: (1) For the following input of shape `[1, 2, 2, 1]` and block_size of 2: -```prettyprint +``` x = [[[[1], [2]], [[3], [4]]]] ``` The output tensor has shape `[4, 1, 1, 1]` and value: -```prettyprint +``` [[[[1]]], [[[2]]], [[[3]]], [[[4]]]] ``` (2) For the following input of shape `[1, 2, 2, 3]` and block_size of 2: -```prettyprint +``` x = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]] ``` The output tensor has shape `[4, 1, 1, 3]` and value: -```prettyprint +``` [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]] ``` (3) For the following input of shape `[1, 4, 4, 1]` and block_size of 2: -```prettyprint +``` x = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]], [[9], [10], [11], [12]], @@ -3508,7 +3508,7 @@ x = [[[[1], [2], [3], [4]], The output tensor has shape `[4, 2, 2, 1]` and value: -```prettyprint +``` x = [[[[1], [3]], [[9], [11]]], [[[2], [4]], [[10], [12]]], [[[5], [7]], [[13], [15]]], @@ -3517,7 +3517,7 @@ x = [[[[1], [3]], [[9], [11]]], (4) For the following input of shape `[2, 2, 4, 1]` and block_size of 2: -```prettyprint +``` x = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]]], [[[9], [10], [11], [12]], @@ -3526,7 +3526,7 @@ x = [[[[1], [2], [3], [4]], The output tensor has shape `[8, 1, 2, 1]` and value: -```prettyprint +``` x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]], [[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]] ``` @@ -3612,26 +3612,26 @@ Some examples: (1) For the following input of shape `[4, 1, 1, 1]`, `block_shape = [2, 2]`, and `crops = [[0, 0], [0, 0]]`: -```prettyprint +``` [[[[1]]], [[[2]]], [[[3]]], [[[4]]]] ``` The output tensor has shape `[1, 2, 2, 1]` and value: -```prettyprint +``` x = [[[[1], [2]], [[3], [4]]]] ``` (2) For the following input of shape `[4, 1, 1, 3]`, `block_shape = [2, 2]`, and `crops = [[0, 0], [0, 0]]`: -```prettyprint +``` [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]] ``` The output tensor has shape `[1, 2, 2, 3]` and value: -```prettyprint +``` x = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]] ``` @@ -3639,7 +3639,7 @@ x = [[[[1, 2, 3], [4, 5, 6]], (3) For the following input of shape `[4, 2, 2, 1]`, `block_shape = [2, 2]`, and `crops = [[0, 0], [0, 0]]`: -```prettyprint +``` x = [[[[1], [3]], [[9], [11]]], [[[2], [4]], [[10], [12]]], [[[5], [7]], [[13], [15]]], @@ -3648,7 +3648,7 @@ x = [[[[1], [3]], [[9], [11]]], The output tensor has shape `[1, 4, 4, 1]` and value: -```prettyprint +``` x = [[[1], [2], [3], [4]], [[5], [6], [7], [8]], [[9], [10], [11], [12]], @@ -3658,7 +3658,7 @@ x = [[[1], [2], [3], [4]], (4) For the following input of shape `[8, 1, 3, 1]`, `block_shape = [2, 2]`, and `crops = [[0, 0], [2, 0]]`: -```prettyprint +``` x = [[[[0], [1], [3]]], [[[0], [9], [11]]], [[[0], [2], [4]]], [[[0], [10], [12]]], [[[0], [5], [7]]], [[[0], [13], [15]]], @@ -3667,7 +3667,7 @@ x = [[[[0], [1], [3]]], [[[0], [9], [11]]], The output tensor has shape `[2, 2, 4, 1]` and value: -```prettyprint +``` x = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]]], [[[9], [10], [11], [12]], @@ -3732,32 +3732,32 @@ Some examples: (1) For the following input of shape `[4, 1, 1, 1]` and block_size of 2: -```prettyprint +``` [[[[1]]], [[[2]]], [[[3]]], [[[4]]]] ``` The output tensor has shape `[1, 2, 2, 1]` and value: -```prettyprint +``` x = [[[[1], [2]], [[3], [4]]]] ``` (2) For the following input of shape `[4, 1, 1, 3]` and block_size of 2: -```prettyprint +``` [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]] ``` The output tensor has shape `[1, 2, 2, 3]` and value: -```prettyprint +``` x = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]] ``` (3) For the following input of shape `[4, 2, 2, 1]` and block_size of 2: -```prettyprint +``` x = [[[[1], [3]], [[9], [11]]], [[[2], [4]], [[10], [12]]], [[[5], [7]], [[13], [15]]], @@ -3766,7 +3766,7 @@ x = [[[[1], [3]], [[9], [11]]], The output tensor has shape `[1, 4, 4, 1]` and value: -```prettyprint +``` x = [[[1], [2], [3], [4]], [[5], [6], [7], [8]], [[9], [10], [11], [12]], @@ -3775,14 +3775,14 @@ x = [[[1], [2], [3], [4]], (4) For the following input of shape `[8, 1, 2, 1]` and block_size of 2: -```prettyprint +``` x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]], [[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]] ``` The output tensor has shape `[2, 2, 4, 1]` and value: -```prettyprint +``` x = [[[[1], [3]], [[5], [7]]], [[[2], [4]], [[10], [12]]], [[[5], [7]], [[13], [15]]], @@ -3848,14 +3848,14 @@ purely convolutional models. For example, given this input of shape `[1, 2, 2, 1]`, and block_size of 2: -```prettyprint +``` x = [[[[1], [2]], [[3], [4]]]] ``` This operation will output a tensor of shape `[1, 1, 1, 4]`: -```prettyprint +``` [[[[1, 2, 3, 4]]]] ``` @@ -3866,7 +3866,7 @@ The output element shape is `[1, 1, 4]`. For an input tensor with larger depth, here of shape `[1, 2, 2, 3]`, e.g. -```prettyprint +``` x = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]] ``` @@ -3874,13 +3874,13 @@ x = [[[[1, 2, 3], [4, 5, 6]], This operation, for block_size of 2, will return the following tensor of shape `[1, 1, 1, 12]` -```prettyprint +``` [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]] ``` Similarly, for the following input of shape `[1 4 4 1]`, and a block size of 2: -```prettyprint +``` x = [[[[1], [2], [5], [6]], [[3], [4], [7], [8]], [[9], [10], [13], [14]], @@ -3889,7 +3889,7 @@ x = [[[[1], [2], [5], [6]], the operator will return the following tensor of shape `[1 2 2 4]`: -```prettyprint +``` x = [[[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12], @@ -3958,14 +3958,14 @@ purely convolutional models. For example, given this input of shape `[1, 1, 1, 4]`, and a block size of 2: -```prettyprint +``` x = [[[[1, 2, 3, 4]]]] ``` This operation will output a tensor of shape `[1, 2, 2, 1]`: -```prettyprint +``` [[[[1], [2]], [[3], [4]]]] ``` @@ -3977,14 +3977,14 @@ The output element shape is `[2, 2, 1]`. For an input tensor with larger depth, here of shape `[1, 1, 1, 12]`, e.g. -```prettyprint +``` x = [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]] ``` This operation, for block size of 2, will return the following tensor of shape `[1, 2, 2, 3]` -```prettyprint +``` [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]] @@ -3992,7 +3992,7 @@ This operation, for block size of 2, will return the following tensor of shape Similarly, for the following input of shape `[1 2 2 4]`, and a block size of 2: -```prettyprint +``` x = [[[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12], @@ -4001,7 +4001,7 @@ x = [[[[1, 2, 3, 4], the operator will return the following tensor of shape `[1 4 4 1]`: -```prettyprint +``` x = [[ [1], [2], [5], [6]], [ [3], [4], [7], [8]], [ [9], [10], [13], [14]], @@ -4775,7 +4775,7 @@ index. For example, say we want to insert 4 scattered elements in a rank-1 tensor with 8 elements.
- +
In Python, this scatter operation would look like this: @@ -4798,7 +4798,7 @@ example, if we wanted to insert two slices in the first dimension of a rank-3 tensor with two matrices of new values.
- +
In Python, this scatter operation would look like this: diff --git a/tensorflow/core/ops/data_flow_ops.cc b/tensorflow/core/ops/data_flow_ops.cc index 8b9c92859d7..b34dd4ae90b 100644 --- a/tensorflow/core/ops/data_flow_ops.cc +++ b/tensorflow/core/ops/data_flow_ops.cc @@ -102,7 +102,7 @@ For example: ```
- +
partitions: Any shape. Indices in the range `[0, num_partitions)`. @@ -190,7 +190,7 @@ For example: ```
- +
)doc"); diff --git a/tensorflow/core/ops/resource_variable_ops.cc b/tensorflow/core/ops/resource_variable_ops.cc index c190b81dde3..c060aa6be91 100644 --- a/tensorflow/core/ops/resource_variable_ops.cc +++ b/tensorflow/core/ops/resource_variable_ops.cc @@ -295,7 +295,7 @@ the same location, their contributions add. Requires `updates.shape = indices.shape + ref.shape[1:]`.
- +
resource: Should be from a `Variable` node. diff --git a/tensorflow/core/ops/state_ops.cc b/tensorflow/core/ops/state_ops.cc index cfb3ea71411..0890d5fc7c7 100644 --- a/tensorflow/core/ops/state_ops.cc +++ b/tensorflow/core/ops/state_ops.cc @@ -288,7 +288,7 @@ for each value is undefined. Requires `updates.shape = indices.shape + ref.shape[1:]`.
- +
ref: Should be from a `Variable` node. @@ -332,7 +332,7 @@ the same location, their contributions add. Requires `updates.shape = indices.shape + ref.shape[1:]`.
- +
ref: Should be from a `Variable` node. @@ -376,7 +376,7 @@ the same location, their (negated) contributions add. Requires `updates.shape = indices.shape + ref.shape[1:]`.
- +
ref: Should be from a `Variable` node. diff --git a/tensorflow/docs_src/api_guides/python/contrib.integrate.md b/tensorflow/docs_src/api_guides/python/contrib.integrate.md index e6b730b2035..e95b5a2e686 100644 --- a/tensorflow/docs_src/api_guides/python/contrib.integrate.md +++ b/tensorflow/docs_src/api_guides/python/contrib.integrate.md @@ -33,7 +33,7 @@ plt.plot(x, z) ```
- +
## Ops diff --git a/tensorflow/docs_src/extend/architecture.md b/tensorflow/docs_src/extend/architecture.md index 42721eb488c..21816502ace 100644 --- a/tensorflow/docs_src/extend/architecture.md +++ b/tensorflow/docs_src/extend/architecture.md @@ -25,7 +25,7 @@ The TensorFlow runtime is a cross-platform library. Figure 1 illustrates its general architecture. A C API separates user level code in different languages from the core runtime. -![TensorFlow Layers](../images/layers.png){: width="300"} +![TensorFlow Layers](https://www.tensorflow.org/images/layers.png){: width="300"} **Figure 1** @@ -57,7 +57,7 @@ Other tasks send updates to these parameters as they work on optimizing the parameters. This particular division of labor between tasks is not required, but it is common for distributed training. -![TensorFlow Architecture Diagram](../images/diag1.svg){: width="500"} +![TensorFlow Architecture Diagram](https://www.tensorflow.org/images/diag1.svg){: width="500"} **Figure 2** @@ -91,7 +91,7 @@ In Figure 3, the client has built a graph that applies weights (w) to a feature vector (x), adds a bias term (b) and saves the result in a variable (s). -![TensorFlow Architecture Diagram: Client](../images/graph_client.svg){: width="700"} +![TensorFlow Architecture Diagram: Client](https://www.tensorflow.org/images/graph_client.svg){: width="700"} **Figure 3** @@ -114,7 +114,7 @@ a step, it applies standard optimizations such as common subexpression elimination and constant folding. It then coordinates execution of the optimized subgraphs across a set of tasks. -![TensorFlow Architecture Diagram: Master](../images/graph_master_cln.svg){: width="700"} +![TensorFlow Architecture Diagram: Master](https://www.tensorflow.org/images/graph_master_cln.svg){: width="700"} **Figure 4** @@ -123,7 +123,7 @@ Figure 5 shows a possible partition of our example graph. The distributed master has grouped the model parameters in order to place them together on the parameter server. -![Partitioned Graph](../images/graph_split1.svg){: width="700"} +![Partitioned Graph](https://www.tensorflow.org/images/graph_split1.svg){: width="700"} **Figure 5** @@ -132,14 +132,14 @@ Where graph edges are cut by the partition, the distributed master inserts send and receive nodes to pass information between the distributed tasks (Figure 6). -![Partitioned Graph](../images/graph_split2.svg){: width="700"} +![Partitioned Graph](https://www.tensorflow.org/images/graph_split2.svg){: width="700"} **Figure 6** The distributed master then ships the graph pieces to the distributed tasks. -![Partitioned Graph](../images/graph_workers_cln.svg){: width="700"} +![Partitioned Graph](https://www.tensorflow.org/images/graph_workers_cln.svg){: width="700"} **Figure 7** @@ -181,7 +181,7 @@ We also have preliminary support for NVIDIA's NCCL library for multi-GPU communication (see [`tf.contrib.nccl`]( https://www.tensorflow.org/code/tensorflow/contrib/nccl/python/ops/nccl_ops.py)). -![Partitioned Graph](../images/graph_send_recv.svg){: width="700"} +![Partitioned Graph](https://www.tensorflow.org/images/graph_send_recv.svg){: width="700"} **Figure 8** diff --git a/tensorflow/docs_src/extend/estimators.md b/tensorflow/docs_src/extend/estimators.md index 28f62e01ab0..c5444c59cab 100644 --- a/tensorflow/docs_src/extend/estimators.md +++ b/tensorflow/docs_src/extend/estimators.md @@ -72,7 +72,7 @@ for abalone: The label to predict is number of rings, as a proxy for abalone age. -![Abalone shell](../images/abalone_shell.jpg) **[“Abalone +![Abalone shell](https://www.tensorflow.org/abalone_shell.jpg) **[“Abalone shell”](https://www.flickr.com/photos/thenickster/16641048623/) (by [Nicki Dugan Pogue](https://www.flickr.com/photos/thenickster/), CC BY-SA 2.0)** diff --git a/tensorflow/docs_src/get_started/embedding_viz.md b/tensorflow/docs_src/get_started/embedding_viz.md index f512d5d809b..84245b11bea 100644 --- a/tensorflow/docs_src/get_started/embedding_viz.md +++ b/tensorflow/docs_src/get_started/embedding_viz.md @@ -21,7 +21,7 @@ interested in word embeddings, gives a good introduction. @@ -173,7 +173,7 @@ last data point in the bottom right: Note in the example above that the last row doesn't have to be filled. For a concrete example of a sprite, see -[this sprite image](../images/mnist_10k_sprite.png) of 10,000 MNIST digits +[this sprite image](https://www.tensorflow.org/images/mnist_10k_sprite.png) of 10,000 MNIST digits (100x100). Note: We currently support sprites up to 8192px X 8192px. @@ -247,7 +247,7 @@ further analysis on their own with the "Isolate Points" button in the Inspector pane on the right hand side. -![Selection of nearest neighbors](../images/embedding-nearest-points.png "Selection of nearest neighbors") +![Selection of nearest neighbors](https://www.tensorflow.org/images/embedding-nearest-points.png "Selection of nearest neighbors") *Selection of the nearest neighbors of “important” in a word embedding dataset.* The combination of filtering with custom projection can be powerful. Below, we filtered @@ -260,10 +260,10 @@ You can see that on the right side we have “ideas”, “science”, “perspe @@ -284,4 +284,4 @@ projection) as a small file. The Projector can then be pointed to a set of one or more of these files, producing the panel below. Other users can then walk through a sequence of bookmarks. -Bookmark panel +Bookmark panel diff --git a/tensorflow/docs_src/get_started/get_started.md b/tensorflow/docs_src/get_started/get_started.md index 6bee7529d0a..b52adc3790a 100644 --- a/tensorflow/docs_src/get_started/get_started.md +++ b/tensorflow/docs_src/get_started/get_started.md @@ -123,7 +123,7 @@ TensorFlow provides a utility called TensorBoard that can display a picture of the computational graph. Here is a screenshot showing how TensorBoard visualizes the graph: -![TensorBoard screenshot](../images/getting_started_add.png) +![TensorBoard screenshot](https://www.tensorflow.org/images/getting_started_add.png) As it stands, this graph is not especially interesting because it always produces a constant result. A graph can be parameterized to accept external @@ -154,7 +154,7 @@ resulting in the output In TensorBoard, the graph looks like this: -![TensorBoard screenshot](../images/getting_started_adder.png) +![TensorBoard screenshot](https://www.tensorflow.org/images/getting_started_adder.png) We can make the computational graph more complex by adding another operation. For example, @@ -170,7 +170,7 @@ produces the output The preceding computational graph would look as follows in TensorBoard: -![TensorBoard screenshot](../images/getting_started_triple.png) +![TensorBoard screenshot](https://www.tensorflow.org/images/getting_started_triple.png) In machine learning we will typically want a model that can take arbitrary inputs, such as the one above. To make the model trainable, we need to be able @@ -336,7 +336,7 @@ program your loss will not be exactly the same, because the model is initialized with random values. This more complicated program can still be visualized in TensorBoard -![TensorBoard final model visualization](../images/getting_started_final.png) +![TensorBoard final model visualization](https://www.tensorflow.org/images/getting_started_final.png) ## `tf.contrib.learn` diff --git a/tensorflow/docs_src/get_started/graph_viz.md b/tensorflow/docs_src/get_started/graph_viz.md index b69103299ea..06ec427b757 100644 --- a/tensorflow/docs_src/get_started/graph_viz.md +++ b/tensorflow/docs_src/get_started/graph_viz.md @@ -2,7 +2,7 @@ TensorFlow computation graphs are powerful but complicated. The graph visualization can help you understand and debug them. Here's an example of the visualization at work. -![Visualization of a TensorFlow graph](../images/graph_vis_animation.gif "Visualization of a TensorFlow graph") +![Visualization of a TensorFlow graph](https://www.tensorflow.org/images/graph_vis_animation.gif "Visualization of a TensorFlow graph") *Visualization of a TensorFlow graph.* To see your own graph, run TensorBoard pointing it to the log directory of the job, click on the graph tab on the top pane and select the appropriate run using the menu at the upper left corner. For in depth information on how to run TensorBoard and make sure you are logging all the necessary information, see @{$summaries_and_tensorboard$TensorBoard: Visualizing Learning}. @@ -43,10 +43,10 @@ expanded states.
- Custom controls panel + Custom controls panel - Custom projection + Custom projection
@@ -87,10 +87,10 @@ and the auxiliary area.
- Unexpanded name scope + Unexpanded name scope - Expanded name scope + Expanded name scope
@@ -114,10 +114,10 @@ specific set of nodes.
- conv_1 is part of the main graph + conv_1 is part of the main graph - save is extracted as auxiliary node + save is extracted as auxiliary node
@@ -135,15 +135,15 @@ for constants and summary nodes. To summarize, here's a table of node symbols: Symbol | Meaning --- | --- -![Name scope](../images/namespace_node.png "Name scope") | *High-level* node representing a name scope. Double-click to expand a high-level node. -![Sequence of unconnected nodes](../images/horizontal_stack.png "Sequence of unconnected nodes") | Sequence of numbered nodes that are not connected to each other. -![Sequence of connected nodes](../images/vertical_stack.png "Sequence of connected nodes") | Sequence of numbered nodes that are connected to each other. -![Operation node](../images/op_node.png "Operation node") | An individual operation node. -![Constant node](../images/constant.png "Constant node") | A constant. -![Summary node](../images/summary.png "Summary node") | A summary node. -![Data flow edge](../images/dataflow_edge.png "Data flow edge") | Edge showing the data flow between operations. -![Control dependency edge](../images/control_edge.png "Control dependency edge") | Edge showing the control dependency between operations. -![Reference edge](../images/reference_edge.png "Reference edge") | A reference edge showing that the outgoing operation node can mutate the incoming tensor. +![Name scope](https://www.tensorflow.org/images/namespace_node.png "Name scope") | *High-level* node representing a name scope. Double-click to expand a high-level node. +![Sequence of unconnected nodes](https://www.tensorflow.org/images/horizontal_stack.png "Sequence of unconnected nodes") | Sequence of numbered nodes that are not connected to each other. +![Sequence of connected nodes](https://www.tensorflow.org/images/vertical_stack.png "Sequence of connected nodes") | Sequence of numbered nodes that are connected to each other. +![Operation node](https://www.tensorflow.org/images/op_node.png "Operation node") | An individual operation node. +![Constant node](https://www.tensorflow.org/images/constant.png "Constant node") | A constant. +![Summary node](https://www.tensorflow.org/images/summary.png "Summary node") | A summary node. +![Data flow edge](https://www.tensorflow.org/images/dataflow_edge.png "Data flow edge") | Edge showing the data flow between operations. +![Control dependency edge](https://www.tensorflow.org/images/control_edge.png "Control dependency edge") | Edge showing the control dependency between operations. +![Reference edge](https://www.tensorflow.org/images/reference_edge.png "Reference edge") | A reference edge showing that the outgoing operation node can mutate the incoming tensor. ## Interaction {#interaction} @@ -161,10 +161,10 @@ right corner of the visualization.
- Sequence of nodes + Sequence of nodes - Expanded sequence of nodes + Expanded sequence of nodes
@@ -207,10 +207,10 @@ The images below give an illustration for a piece of a real-life graph.
- Info card of a name scope + Info card of a name scope - Info card of operation node + Info card of operation node
@@ -233,7 +233,7 @@ The images below show the CIFAR-10 model with tensor shape information:
- Color by structure + Color by structure - Color by device + Color by device
@@ -303,13 +303,13 @@ tensor output sizes.
- CIFAR-10 model with tensor shape information + CIFAR-10 model with tensor shape information
- Color by compute time + Color by compute time - Run metadata graph + Run metadata graph - Run metadata info card + Run metadata info card
diff --git a/tensorflow/docs_src/get_started/mnist/beginners.md b/tensorflow/docs_src/get_started/mnist/beginners.md index 2da2c19ea60..624d9164748 100644 --- a/tensorflow/docs_src/get_started/mnist/beginners.md +++ b/tensorflow/docs_src/get_started/mnist/beginners.md @@ -15,7 +15,7 @@ MNIST is a simple computer vision dataset. It consists of images of handwritten digits like these:
- +
It also includes labels for each image, telling us which digit it is. For @@ -88,7 +88,7 @@ Each image is 28 pixels by 28 pixels. We can interpret this as a big array of numbers:
- +
We can flatten this array into a vector of 28x28 = 784 numbers. It doesn't @@ -110,7 +110,7 @@ Each entry in the tensor is a pixel intensity between 0 and 1, for a particular pixel in a particular image.
- +
Each image in MNIST has a corresponding label, a number between 0 and 9 @@ -124,7 +124,7 @@ vector which is 1 in the \\(n\\)th dimension. For example, 3 would be `[55000, 10]` array of floats.
- +
We're now ready to actually make our model! @@ -157,7 +157,7 @@ classes. Red represents negative weights, while blue represents positive weights.
- +
We also add some extra evidence called a bias. Basically, we want to be able @@ -202,13 +202,13 @@ although with a lot more \\(x\\)s. For each output, we compute a weighted sum of the \\(x\\)s, add a bias, and then apply softmax.
- +
If we write that out as equations, we get:
-[y1, y2, y3] = softmax(W11*x1 + W12*x2 + W13*x3 + b1, W21*x1 + W22*x2 + W23*x3 + b2, W31*x1 + W32*x2 + W33*x3 + b3)
@@ -217,7 +217,7 @@ and vector addition. This is helpful for computational efficiency. (It's also a useful way to think.)
-[y1, y2, y3] = softmax([[W11, W12, W13], [W21, W22, W23], [W31, W32, W33]]*[x1, x2, x3] + [b1, b2, b3])
diff --git a/tensorflow/docs_src/get_started/mnist/mechanics.md b/tensorflow/docs_src/get_started/mnist/mechanics.md index b55a5c19ff9..48d9a395f28 100644 --- a/tensorflow/docs_src/get_started/mnist/mechanics.md +++ b/tensorflow/docs_src/get_started/mnist/mechanics.md @@ -34,7 +34,7 @@ MNIST is a classic problem in machine learning. The problem is to look at greyscale 28x28 pixel images of handwritten digits and determine which digit the image represents, for all the digits from zero to nine. -![MNIST Digits](../../images/mnist_digits.png "MNIST Digits") +![MNIST Digits](https://www.tensorflow.org/images/mnist_digits.png "MNIST Digits") For more information, refer to [Yann LeCun's MNIST page](http://yann.lecun.com/exdb/mnist/) or [Chris Olah's visualizations of MNIST](http://colah.github.io/posts/2014-10-Visualizing-MNIST/). @@ -90,7 +90,7 @@ loss. and apply gradients.
- +
### Inference @@ -384,7 +384,7 @@ summary_writer.add_summary(summary_str, step) When the events files are written, TensorBoard may be run against the training folder to display the values from the summaries. -![MNIST TensorBoard](../../images/mnist_tensorboard.png "MNIST TensorBoard") +![MNIST TensorBoard](https://www.tensorflow.org/images/mnist_tensorboard.png "MNIST TensorBoard") **NOTE**: For more info about how to build and run Tensorboard, please see the accompanying tutorial @{$summaries_and_tensorboard$Tensorboard: Visualizing Learning}. diff --git a/tensorflow/docs_src/get_started/monitors.md b/tensorflow/docs_src/get_started/monitors.md index 7db88c89812..cb4ef70eebf 100644 --- a/tensorflow/docs_src/get_started/monitors.md +++ b/tensorflow/docs_src/get_started/monitors.md @@ -401,6 +401,6 @@ Then navigate to `http://0.0.0.0:`*``* in your browser, where If you click on the accuracy field, you'll see an image like the following, which shows accuracy plotted against step count: -![Accuracy over step count in TensorBoard](../images/validation_monitor_tensorboard_accuracy.png "Accuracy over step count in TensorBoard") +![Accuracy over step count in TensorBoard](https://www.tensorflow.org/images/validation_monitor_tensorboard_accuracy.png "Accuracy over step count in TensorBoard") For more on using TensorBoard, see @{$summaries_and_tensorboard$TensorBoard: Visualizing Learning} and @{$graph_viz$TensorBoard: Graph Visualization}. diff --git a/tensorflow/docs_src/get_started/summaries_and_tensorboard.md b/tensorflow/docs_src/get_started/summaries_and_tensorboard.md index 6e06c9e41e4..45d43e7a6e7 100644 --- a/tensorflow/docs_src/get_started/summaries_and_tensorboard.md +++ b/tensorflow/docs_src/get_started/summaries_and_tensorboard.md @@ -8,7 +8,7 @@ your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. When TensorBoard is fully configured, it looks like this: -![MNIST TensorBoard](../images/mnist_tensorboard.png "MNIST TensorBoard") +![MNIST TensorBoard](https://www.tensorflow.org/images/mnist_tensorboard.png "MNIST TensorBoard")