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@muriel.schmidt

In Tensorflow, you can verify the input data in a few ways. Here are a couple of approaches you can use:

**Assert statements**: You can add assert statements in your Tensorflow code to check the shape, type, range, or any other criteria of the input data. For example:

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import tensorflow as tf input_data = tf.placeholder(tf.float32, shape=(None, 10)) # Assert that the input data must have a certain shape assert input_data.shape[1] == 10 # Assert that the input data must be of a certain type assert input_data.dtype == tf.float32 # Assert any other conditions on the input data |

**Input data pipeline**: Another way to verify input data is by using a data pipeline, such as tf.data.Dataset. You can perform data preprocessing steps, data augmentation, and data validation as part of the data pipeline. For example:

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import tensorflow as tf import numpy as np # Create a dataset from input data input_data = np.random.rand(100, 10) dataset = tf.data.Dataset.from_tensor_slices(input_data) # Verify the input data within the data pipeline def preprocess_data(data): # Assert the shape of the input data assert data.shape[0] == 10 return data dataset = dataset.map(preprocess_data) |

By incorporating these techniques into your Tensorflow code, you can ensure that the input data meets the required criteria before feeding it into the model.