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1 output in the case of some image processing tasks. Dimension of data space. 1 or more outputs (required if using the fit function). Select output name. Data size. Data attributes. Data is compressed (using the GZIP format). Data has some special attributes. 1 input or more outputs. All data (generic input and output format). Dimensions of the input data. Dimensions of the output data. Dimensions of the time (task or epoch) output. Dimensions of the channels (input or output). For each channel, either 1 input, 1 output, or 1 for each task/epoch. 1 input or 1 output (required if using the fit function). 1 or more input or output (required if using the fit function). Data with format 'csv' or 'tsv'. Data with format'mat' or'mha'. Data with format 'hdf5'. Data with format 'tensorflow'. Data with format 'excel' or 'csv' or'mat' or'mha' or 'hdf5' or 'tensorflow' or 'txt'. Modules and neural networks Data in this module are recorded with the Keras API. The Keras API lets you access the model and the trained data of the model using the API. This means you can select different modules or neural networks to build the model and have access to their training data in memory. Below we list some useful functions available in the Keras API: Check whether the model was created using the 'tf.keras' backend. If not, convert it to 'tf.keras'. Initialize the 'TF_CONFIG' environment variable. For more information, refer to the documentation: Keras API. Check whether the model has been converted to a Gluon model using the 'convert_keras_model_to_gluon_model()' method. If the Gluon model is created, convert it to a Gluon API model: Remove the model. Check whether the model has been converted to a Gluon model using the 'convert_keras_model_to_gluon_model()' method. If the Gluon model is created, convert it to a Gluon API model: Allocate the model. Check whether the model is a Keras model or a Gluon model. If the model is a Keras model, convert it to a Gluon API model. If the model is a Gluon
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