Help protect the Great Barrier Reef with TensorFlow on Kaggle

# tf.nn.conv2d

Computes a 2-D convolution given `input` and 4-D `filters` tensors.

The `input` tensor may have rank `4` or higher, where shape dimensions `[:-3]` are considered batch dimensions (`batch_shape`).

Given an input tensor of shape `batch_shape + [in_height, in_width, in_channels]` and a filter / kernel tensor of shape `[filter_height, filter_width, in_channels, out_channels]`, this op performs the following:

1. Flattens the filter to a 2-D matrix with shape `[filter_height * filter_width * in_channels, output_channels]`.
2. Extracts image patches from the input tensor to form a virtual tensor of shape ```[batch, out_height, out_width, filter_height * filter_width * in_channels]```.
3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

``````output[b, i, j, k] =
sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] *
filter[di, dj, q, k]
``````

Must have `strides[0] = strides[3] = 1`. For the most common case of the same horizontal and vertical strides, `strides = [1, stride, stride, 1]`.

#### Usage Example:

````x_in = np.array([[`
`  [[2], [1], [2], [0], [1]],`
`  [[1], [3], [2], [2], [3]],`
`  [[1], [1], [3], [3], [0]],`
`  [[2], [2], [0], [1], [1]],`
`  [[0], [0], [3], [1], [2]], ]])`
`kernel_in = np.array([`
` [ [[2, 0.1]], [[3, 0.2]] ],`
` [ [[0, 0.3]],[[1, 0.4]] ], ])`
`x = tf.constant(x_in, dtype=tf.float32)`
`kernel = tf.constant(kernel_in, dtype=tf.float32)`
`tf.nn.conv2d(x, kernel, strides=[1, 1, 1, 1], padding='VALID')`
`<tf.Tensor: shape=(1, 4, 4, 2), dtype=float32, numpy=..., dtype=float32)>`
```

`input` A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. A Tensor of rank at least 4. The dimension order is interpreted according to the value of `data_format`; with the all-but-inner-3 dimensions acting as batch dimensions. See below for details.
`filters` A `Tensor`. Must have the same type as `input`. A 4-D tensor of shape `[filter_height, filter_width, in_channels, out_channels]`
`strides` An int or list of `ints` that has length `1`, `2` or `4`. The stride of the sliding window for each dimension of `input`. If a single value is given it is replicated in the `H` and `W` dimension. By default the `N` and `C` dimensions are set to 1. The dimension order is determined by the value of `data_format`, see below for details.
`padding` Either the `string` `"SAME"` or `"VALID"` indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. See here for more information. When explicit padding is used and data_format is `"NHWC"`, this should be in the form ```[[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]```. When explicit padding used and data_format is `"NCHW"`, this should be in the form ```[[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]]```.
`data_format` An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: `batch_shape + [height, width, channels]`. Alternatively, the format could be "NCHW", the data storage order of: `batch_shape + [channels, height, width]`.
`dilations` An int or list of `ints` that has length `1`, `2` or `4`, defaults to 1. The dilation factor for each dimension of`input`. If a single value is given it is replicated in the `H` and `W` dimension. By default the `N` and `C` dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
`name` A name for the operation (optional).

A `Tensor`. Has the same type as `input` and the same outer batch shape.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]