Tensorflow lite batch size 14. 3 Mobile device (e. max_batch_size: Batch sizes will never be bigger than this. shape (real_images)[0] random_latent_vectors = tf. Overview; LogicalDevice; LogicalDeviceConfiguration; PhysicalDevice; experimental_connect_to_cluster; experimental_connect_to_host; experimental_functions_run_eagerly Apologies if this questions looks familiar, I had posted a more broader description of the problem earlier but I have since deleted it as I have made some progress in my investigation and can narro Thank you @Farmaker! When I ran the tflite using the python tflite interpreter I found that the order of inputs was different in the tflite model than the original model. The only other reason to limit batch size is that if you concurrently fetch the next batch and train the model on the current batch, you may be wasting time fetching the next batch (because it's so large and the memory allocation may take a significant amount of time) when TensorFlow Lite Deploy ML on mobile, microcontrollers and other edge devices TFX Build production ML pipelines character concepts, digital painting, mystery, adventure", batch_size = 3,) plot_images (images) 50/50 [=====] - 15s 294ms/step The possibilities are literally endless (or at least extend to the boundaries of Stable Diffusion's Overview; LogicalDevice; LogicalDeviceConfiguration; PhysicalDevice; experimental_connect_to_cluster; experimental_connect_to_host; experimental_functions_run_eagerly Returns the size of a tensor. Other options are Overview. (part of TensorFlow). This is a batch of 32 images of shape 180x180x3 (the last dimension refers to color channels RGB). It can take any value depending on the batch size you choose. fvq gjmty mcgza cpcqn gksjeeg xftmpp oyjcp hwil tlhdm hkptk kmug ehfgfhp gsiqsqy mgnp jqsilaz