Fits the state of the preprocessing layer to the data being passed.
Inception-ResNet v2 model, with weights trained on ImageNet
Inception V3 model, with weights pre-trained on ImageNet.
Callback that streams epoch results to a csv file
Stop training when a monitored quantity has stopped improving.
Reduce learning rate when a metric has stopped improving.
Callback used to stream events to a server.
Callback that terminates training when a NaN loss is encountered.
Count the total number of scalars composing the weights.
Fashion-MNIST database of fashion articles
IMDB Movie reviews sentiment classification
Evaluates the model on a data generator.
Fits the model on data yielded batch-by-batch by a generator.
Fit image data generator internal statistics to some sample data.
Update tokenizer internal vocabulary based on a list of texts or list of sequences.
Generates batches of augmented/normalized data from image data and labels
Takes the dataframe and the path to a directory and generates batches of augmented/normalized data.
Generates batches of data from images in a directory (with optional augmented/normalized data)
Downloads a file from a URL if it not already in the cache.
Retrieve tensors for layers with multiple nodes
Retrieves a layer based on either its name (unique) or index.
Get the vocabulary for text vectorization layers
Representation of HDF5 dataset to be used instead of an R array
Generate batches of image data with real-time data augmentation. The data will be looped over (in batches).
Decodes the prediction of an ImageNet model.
Preprocesses a tensor or array encoding a batch of images.
Initializer that generates tensors initialized to a constant value.
Glorot normal initializer, also called Xavier normal initializer.
Glorot uniform initializer, also called Xavier uniform initializer.
He uniform variance scaling initializer.
Initializer that generates the identity matrix.
Initializer that generates tensors initialized to 1.
Initializer that generates a random orthogonal matrix.
Initializer that generates tensors with a normal distribution.
Initializer that generates tensors with a uniform distribution.
Initializer that generates a truncated normal distribution.
Initializer capable of adapting its scale to the shape of weights.
Initializer that generates tensors initialized to 0.
Install Keras and the TensorFlow backend
Creates a 1D tensor containing a sequence of integers.
Returns the index of the maximum value along an axis.
Returns the index of the minimum value along an axis.
Turn a nD tensor into a 2D tensor with same 1st dimension.
Returns the value of more than one tensor variable.
Applies batch normalization on x given mean, var, beta and gamma.
Sets the values of many tensor variables at once.
Binary crossentropy between an output tensor and a target tensor.
Casts a tensor to a different dtype and returns it.
Cast an array to the default Keras float type.
Categorical crossentropy between an output tensor and a target tensor.
Destroys the current TF graph and creates a new one.
Concatenates a list of tensors alongside the specified axis.
2D deconvolution (i.e. transposed convolution).
3D deconvolution (i.e. transposed convolution).
Returns the static number of elements in a Keras variable or tensor.
Runs CTC loss algorithm on each batch element.
Converts CTC labels from dense to sparse.
Cumulative product of the values in a tensor, alongside the specified axis.
Cumulative sum of the values in a tensor, alongside the specified axis.
Depthwise 2D convolution with separable filters.
Multiplies 2 tensors (and/or variables) and returns a _tensor_.
Sets entries in 'x' to zero at random, while scaling the entire tensor.
Returns the dtype of a Keras tensor or variable, as a string.
Fuzz factor used in numeric expressions.
Element-wise equality between two tensors.
Adds a 1-sized dimension at index 'axis'.
Instantiate an identity matrix and returns it.
Reduce elems using fn to combine them from left to right.
Reduce elems using fn to combine them from right to left.
Retrieves the elements of indices 'indices' in the tensor 'reference'.
Returns the gradients of 'variables' w.r.t. 'loss'.
Segment-wise linear approximation of sigmoid.
Returns a tensor with the same content as the input tensor.
Default image data format convention ('channels_first' or 'channels_last').
Selects 'x' in test phase, and 'alt' otherwise.
Returns whether the 'targets' are in the top 'k' 'predictions'.
Selects 'x' in train phase, and 'alt' otherwise.
Returns the shape of tensor or variable as a list of int or NULL entries.
Returns whether a tensor is a sparse tensor.
Returns whether 'x' is a symbolic tensor.
Normalizes a tensor wrt the L2 norm alongside the specified axis.
Computes log(sum(exp(elements across dimensions of a tensor))).
Sets the manual variable initialization flag.
Map the function fn over the elements elems and return the outputs.
Mean of a tensor, alongside the specified axis.
Compute the moving average of a variable.
Returns the number of axes in a tensor, as an integer.
Computes mean and std for batch then apply batch_normalization on batch.
Element-wise inequality between two tensors.
Computes the one-hot representation of an integer tensor.
Instantiates an all-ones tensor variable and returns it.
Instantiates an all-ones variable of the same shape as another tensor.
Instantiates a placeholder tensor and returns it.
Prints 'message' and the tensor value when evaluated.
Multiplies the values in a tensor, alongside the specified axis.
Returns a tensor with random binomial distribution of values.
Returns a tensor with normal distribution of values.
Instantiates a variable with values drawn from a normal distribution.
Returns a tensor with uniform distribution of values.
Instantiates a variable with values drawn from a uniform distribution.
Repeats the elements of a tensor along an axis.
Reshapes a tensor to the specified shape.
Resizes the images contained in a 4D tensor.
Resizes the volume contained in a 5D tensor.
Reverse a tensor along the specified axes.
Iterates over the time dimension of a tensor
Element-wise rounding to the closest integer.
Sets the learning phase to a fixed value.
Sets the value of a variable, from an R array.
Returns the symbolic shape of a tensor or variable.
Categorical crossentropy with integer targets.
Pads the 2nd and 3rd dimensions of a 4D tensor.
Pads 5D tensor with zeros along the depth, height, width dimensions.
Removes a 1-dimension from the tensor at index 'axis'.
Stacks a list of rank 'R' tensors into a rank 'R+1' tensor.
Standard deviation of a tensor, alongside the specified axis.
Returns 'variables' but with zero gradient w.r.t. every other variable.
Sum of the values in a tensor, alongside the specified axis.
Switches between two operations depending on a scalar value.
Pads the middle dimension of a 3D tensor.
Converts a sparse tensor into a dense tensor and returns it.
Returns a tensor with truncated random normal distribution of values.
Update the value of 'x' by adding 'increment'.
Update the value of 'x' by subtracting 'decrement'.
Variance of a tensor, alongside the specified axis.
Instantiates an all-zeros variable and returns it.
Instantiates an all-zeros variable of the same shape as another tensor.
Keras Model composed of a linear stack of layers
Apply an activation function to an output.
Leaky version of a Rectified Linear Unit.
Rectified Linear Unit activation function
Layer that applies an update to the cost function based input activity.
Average pooling operation for spatial data.
Average pooling operation for 3D data (spatial or spatio-temporal).
Batch normalization layer (Ioffe and Szegedy, 2014).
Layer that concatenates a list of inputs.
1D convolution layer (e.g. temporal convolution).
2D convolution layer (e.g. spatial convolution over images).
Transposed 2D convolution layer (sometimes called Deconvolution).
3D convolution layer (e.g. spatial convolution over volumes).
Transposed 3D convolution layer (sometimes called Deconvolution).
Cropping layer for 1D input (e.g. temporal sequence).
Cropping layer for 2D input (e.g. picture).
Cropping layer for 3D data (e.g. spatial or spatio-temporal).
Fast GRU implementation backed by CuDNN.
Fast LSTM implementation backed by CuDNN.
Add a densely-connected NN layer to an output
Layer that computes a dot product between samples in two tensors.
Turns positive integers (indexes) into dense vectors of fixed size.
Apply multiplicative 1-centered Gaussian noise.
Apply additive zero-centered Gaussian noise.
Global average pooling operation for temporal data.
Global average pooling operation for spatial data.
Global Average pooling operation for 3D data.
Global max pooling operation for temporal data.
Global max pooling operation for spatial data.
Global Max pooling operation for 3D data.
Long Short-Term Memory unit - Hochreiter 1997.
Masks a sequence by using a mask value to skip timesteps.
Max pooling operation for temporal data.
Max pooling operation for 3D data (spatial or spatio-temporal).
Layer that computes the maximum (element-wise) a list of inputs.
Layer that computes the minimum (element-wise) a list of inputs.
Layer that multiplies (element-wise) a list of inputs.
Permute the dimensions of an input according to a given pattern
Fully-connected RNN where the output is to be fed back to input.
Zero-padding layer for 1D input (e.g. temporal sequence).
Zero-padding layer for 2D input (e.g. picture).
Zero-padding layer for 3D data (spatial or spatio-temporal).
Generates a word rank-based probabilistic sampling table.
Load a Keras model from the Saved Model format
Generates predictions for the input samples from a data generator.
Returns predictions for a single batch of samples.
Generates probability or class probability predictions for the input samples.
Save/Load models using SavedModel format
Save/Load model weights using HDF5 files
Save model weights in the SavedModel format
Save a text tokenizer to an external file
Convert a list of sequences into a matrix.
Sets vocabulary (and optionally document frequency) data for the layer
Converts a text to a sequence of indexes in a fixed-size hashing space.
One-hot encode a text into a list of word indexes in a vocabulary of size n.
Convert text to a sequence of words (or tokens).
Transform each text in texts in a sequence of integers.
Transforms each text in texts in a sequence of integers.
Apply a layer to every temporal slice of an input.
Utility function for generating batches of temporal data.
Converts a class vector (integers) to binary class matrix.
Single gradient update or model evaluation over one batch of samples.
Select a Keras implementation and backend
Provide a scope with mappings of names to custom objects