Location>code7788 >text

Python Loading TensorFlow Models

Popularity：622 ℃/2024-08-19 23:00:22

and HDF5 to load TensorFlow models

In order to load a TensorFlow model, we first need to specify the format of the model.TensorFlow supports several model formats, but the two most common are SavedModel and HDF5 (for Keras models). Here, I will give sample code for loading each of these two model formats.

1.1 Loading a TensorFlow model in SavedModel format

SavedModel is TensorFlow's recommended high-level format for saving and loading the entire TensorFlow program, including TensorFlow graphs and checkpoints.

sample code (computing)：

Suppose you already have a trained SavedModel model saved in the./saved_modelCatalog.

import tensorflow as tf

# loaded_model
loaded_model = tf.saved_model.load('. /saved_model')

# View the signature of the model
print(list(loaded_model.()))

# Suppose your model has a signature called `saved_default` and accepts an input called `input
# You can use the model for prediction like this (assuming the input data is x_test)
# Note: x_test here needs to be adjusted based on your model inputs
import numpy as np

# Assume the input is a simple numpy array
x_test = ((1, 28, 28, 1)) # e.g. for MNIST model inputs

# Convert to Tensor
x_test_tensor = tf.convert_to_tensor(x_test, dtype=tf.float32)

# Create a batch, since most models expect batch inputs
input_data = {'input': x_test_tensor}

# Call the model
predictions = loaded_model.signatures['serving_default'](input_data)

# Print the predictions
print(predictions['output'].numpy()) # Note: the 'output' here needs to be adjusted to your model output

1.2 Loading the Keras model in HDF5 format

The HDF5 format is a common format used by Keras (TensorFlow high-level API) for saving and loading models.

sample code (computing)：

Suppose you have a Keras model saved inmodel.h5Documentation.

from import load_model

# Load the model
model = load_model('model.h5')

# View the model structure
()

# Assume you have a set of test data x_test and y_test
# Note: x_test and y_test here need to be adapted to your data set
import numpy as np

x_test = ((10, 28, 28, 1)) # hypothetical input data
y_test = (0, 10, size=(10, 1)) # hypothetical output data

# Use the model to make predictions
predictions = (x_test)

# Print the predictions
print(predictions)

1.3 Attention

  • Make sure that the path to your model file (e.g.'./saved_model'maybe'model.h5') is correct.
  • Depending on your model, you may need to adjust the shape and type of input data.
  • For SavedModel, the model's signature and input/output names may be different and need to be adapted to your specific situation.
  • These examples assume that you already have a model file and corresponding test data. If you are starting from scratch, you will need to train a model and save it first.

Load the SavedModel model in

Loading a SavedModel model in TensorFlow is a relatively straightforward process.SavedModel is a wrapper format for TensorFlow that contains the complete TensorFlow program, including computational graphs (Graphs) and parameters (Variables), as well as one or moreSignatures. These signatures define how inputs are provided to the model and outputs are obtained.

Below are the steps and sample code for loading a SavedModel model in TensorFlow:

2.1 Steps

（1）Determine the path to the SavedModel: First, you need to know in which directory the SavedModel file is saved. This directory should contain asaved_model.pbfile and avariablesCatalog (if the model has variables).

（2）utilizationtf.saved_model.loadfunction loading model: TensorFlow provides atf.saved_model.loadfunction to load the SavedModel.This function accepts the path to the SavedModel as an argument and returns atf.saved_model.Loadobject that contains all the signatures and functions of the model.

（3）Signature of the access model: The loaded model object has asignaturesattribute, which is a dictionary containing all the signatures of the model. Each signature has a unique key (usually theserving_default, but could be named otherwise), the corresponding value is a function that takes input and returns output.

（4）Prediction using models: By calling the function corresponding to the signature and passing in the appropriate input data, you can use the model to make predictions.

2.2 Sample Code

import tensorflow as tf

# Load SavedModel
model_path = '. /path_to_your_saved_model' # replace with your SavedModel path
loaded_model = tf.saved_model.load(model_path)

# Check the signature of the model
print(list(loaded_model.())) # There is usually a 'saved_default'

# Let's say your model has a signature called 'serving_default' and accepts an input called 'input'
# You can use the model for prediction like this (assuming you already have the appropriate input data x_test)

# Note: x_test here needs to be adapted to your model inputs
# Assume x_test is a Tensor or a numpy array that can be converted to a Tensor
import numpy as np

x_test = ((1, 28, 28, 1)) # For example, for an input to a MNIST model

# Convert the numpy array to a Tensor
x_test_tensor = tf.convert_to_tensor(x_test, dtype=tf.float32)

# Create a dictionary that maps the input Tensor to the signed input parameter name (in this case 'input')
# Note: the name 'input' needs to be adapted to your model's signature
input_data = {'input': x_test_tensor}

# Call the model
predictions = loaded_model.signatures['serving_default'](input_data)

# Get the predictions
# Note: the 'output' here needs to be adjusted to your model's output signature
# If your model has multiple outputs, you may need to access multiple keys in the predictions dictionary
predicted_output = predictions['output'].numpy()

# Print the predicted results
print(predicted_output)

Note that the code example above assumes that your model signature has a name calledinputand an input parameter namedoutputof the output parameters. However, in practice, these names may vary depending on your model. Therefore, you need to check your model signature for the correct parameter names. You can do this by printing theloaded_model.signatures['serving_default'].structured_outputs(for some versions of TensorFlow) or check your model training code and save logic for this information.

Example of loading the SavedModel model for prediction in the

Loading a SavedModel model in TensorFlow is a straightforward process that allows you to restore your entire previously saved TensorFlow program, including calculating graphs and weights. Below is a detailed example showing how to load a SavedModel model in TensorFlow and make predictions about it.

First, make sure you have a SavedModel model saved in a directory somewhere. This directory should contain asaved_model.pbfile (or it may not be included in newer versions of TensorFlow, as the graph structure may be stored in thevariablesdirectory in one of the subdirectories), and avariablescatalog, which contains the weights and variables of the model.

3.1 Sample Code

import tensorflow as tf

# Specify the path where the SavedModel is saved
saved_model_path = '. /path_to_your_saved_model' # Replace with the actual path of your SavedModel.

# Load the SavedModel
loaded_model = tf.saved_model.load(saved_model_path)

# View the signature of the model
# Note: SavedModel can have more than one signature, but there is usually a default 'serving_default'
print(list(loaded_model.()))

# Assume that the model has a default 'serving_default' signature and we know its inputs and outputs
# Typically, this information can be specified at the time of saving the model via the inputs and outputs parameters

# Prepare the input data
# Here we use random data as an example, which you will need to adapt to your model's input requirements
import numpy as np

# Suppose the input to the model is a Tensor with shape [batch_size, height, width, channels].
# For example, for the MNIST model, it might be a Tensor of shape [1, 28, 28, 1]
input_data = ((1, 28, 28, 1)).astype(np.float32)

# Convert numpy array to Tensor
input_tensor = tf.convert_to_tensor(input_data)

# Create a dictionary that maps the input Tensor to the signed input parameter names
# Note: 'input_tensor' here needs to be adapted to the input parameter names in your model's signature
# If the input parameter name in the signature is indeed 'input_tensor', leave it unchanged; otherwise, replace it with the correct name
# But in many cases, the default name may be 'input' or something similar
input_dict = {'input': input_tensor} # Assume the input parameter name is 'input'

# Call the model to make a prediction
# Use the function corresponding to the signature and pass in the input dictionary
predictions = loaded_model.signatures['serving_default'](input_dict)

# Get the predictions
# The predictions are usually a dictionary containing one or more output Tensor(s)
# 'output' here needs to be adjusted according to the output parameter names in your model's signature
# If there is only one output in the signature and its name is 'output', you can use it directly; otherwise, replace it with the correct key
predicted_output = predictions['output'].numpy()

# Print the predicted results
print(predicted_output)

# Note: If your model has multiple outputs, you need to access each output from the predictions dictionary
# For example: predictions['second_output'].numpy()

3.2 Precautions

（1）Input and output names: In the example above, I'm using theinputcap (a poem)outputas the names of the inputs and outputs. However, these names may not apply to your model. You need to check your model signature to determine the correct input and output parameter names. You can do this by printing theloaded_model.signatures['serving_default'].structured_inputscap (a poem)loaded_model.signatures['serving_default'].structured_outputs(for some versions of TensorFlow) to see this information.

（2）Data types and shapes: Make sure your input data has the data type and shape expected by the model. If the data types or shapes do not match, this may result in an error.

（3）batch file: In the example above, I created a batch containing a single sample. If your model is designed for batch processing and you want to process multiple samples at once, adjust the shape of the input data accordingly.

（4）error handling: In practice, you may need to add error handling logic to handle any exceptions that may occur when loading a model, such as a file that does not exist or an incorrectly formatted model.

<Previous Next>