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site/en/tutorials/load_data/images.ipynb

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},
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"cell_type": "markdown",
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"This model expects it's input to be normalized to the `[-1,1]` range:\n",
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"This model expects its input to be normalized to the `[-1,1]` range:\n",
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"```\n",
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"help(keras_applications.mobilenet_v2.preprocess_input)\n",
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"cell_type": "markdown",
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"So before the passing it to the mobilnet model, we need to convert the input from a range of `[0,1]` to `[-1,1]`."
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"So before the passing it to the MobilNet model, we need to convert the input from a range of `[0,1]` to `[-1,1]`."
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"cell_type": "markdown",
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"The mobilenet returns a `6x6` spatial grid of features for each image.\n",
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"The MobileNet returns a `6x6` spatial grid of features for each image.\n",
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"Pass it a batch of images to see:"
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"So build a model wrapped around mobilenet, and use `tf.keras.layers.GlobalAveragePooling2D` to average over those space dimensions, before the output `tf.keras.layers.Dense` layer:"
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"So build a model wrapped around MobileNet, and use `tf.keras.layers.GlobalAveragePooling2D` to average over those space dimensions, before the output `tf.keras.layers.Dense` layer:"
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"#### Image Strings\n",
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"#### Raw image data\n",
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"\n",
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"TFRecord files are a simple format to store a sequence of binary blobs. In this case the fact that these are read linearly off disk, which may give some performance boost.\n",
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"TFRecord files are a simple format to store a sequence of binary blobs. By packing multiple examples into the same file, TensorFlow is able to read multiple examples at once, which is especially important for performance when using a remote storage service such as GCS.\n",
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"It also gives you a portable data file that is apropriate, for example, for streaming over a network.\n",
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"\n",
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"First, build a `TFRecord` file from the raw image data:"
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"First, build a TFRecord file from the raw image data:"
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