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Last Updated on June 20, 2022
Keras is a simple and powerful Python library for deep learning.
Given that deep learning models can take hours, days and even weeks to train, it is important to know how to save and load them from disk.
In this post, you will discover how you can save your Keras models to file and load them up again to make predictions.
After reading this tutorial you will know:
- How to save model weights and model architecture in separate files.
- How to save model architecture in both YAML and JSON format.
- How to save model weights and architecture into a single file for later use.
Kick-start your project with my new book Deep Learning With Python, including step-by-step tutorials and the Python source code files for all examples.
Let’s get started.
- Update Mar 2017: Added instructions to install h5py first.
- Update Mar/2017: Updated examples for changes to the Keras API.
- Update Mar/2018: Added alternate link to download the dataset.
- Update May/2019: Added section on saving and loading the model to a single file.
- Update Sep/2019: Added note about using PyYAML version 5.
- Update Jun/2022: Added note about deprecated YAML format and added section about protocol buffer.
How to Save and Load Your Keras Deep Learning Models
Photo by art_inthecity, some rights reserved.
Tutorial Overview
If you are new to Keras or deep learning, see this step-by-step Keras tutorial.
Keras separates the concerns of saving your model architecture and saving your model weights.
Model weights are saved to HDF5 format. This is a grid format that is ideal for storing multi-dimensional arrays of numbers.
The model structure can be described and saved using two different formats: JSON and YAML.
In this post we are going to look at three examples of saving and loading your model to file:
- Save Model to JSON.
- Save Model to YAML.
- Save Model to HDF5.
The first two examples save the model architecture and weights separately. The model weights are saved into a HDF5 format file in all cases.
The examples will use the same simple network trained on the Pima Indians onset of diabetes binary classification dataset. This is a small dataset that contains all numerical data and is easy to work with. You can download this dataset and place it in your working directory with the filename “pima-indians-diabetes.csv” (update: download from here).
Confirm that you have TensorFlow v2.x installed (e.g. v2.9 as of June 2022).
Note: Saving models requires that you have the h5py library installed. It is usually installed as a dependency with TensorFlow. You can also install it easily as follows:
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Save Your Neural Network Model to JSON
JSON is a simple file format for describing data hierarchically.
Keras provides the ability to describe any model using JSON format with a to_json() function. This can be saved to file and later loaded via the model_from_json() function that will create a new model from the JSON specification.
The weights are saved directly from the model using the save_weights() function and later loaded using the symmetrical load_weights() function.
The example below trains and evaluates a simple model on the Pima Indians dataset. The model is then converted to JSON format and written to model.json in the local directory. The network weights are written to model.h5 in the local directory.
The model and weight data is loaded from the saved files and a new model is created. It is important to compile the loaded model before it is used. This is so that predictions made using the model can use the appropriate efficient computation from the Keras backend.
The model is evaluated in the same way printing the same evaluation score.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | # MLP for Pima Indians Dataset Serialize to JSON and HDF5 from tensorflow.keras.models import Sequential, model_from_json from tensorflow.keras.layers import Dense import numpy import os # fix random seed for reproducibility numpy.random.seed(7) # load pima indians dataset dataset = numpy.loadtxt(“pima-indians-diabetes.csv”, delimiter=“,”) # split into input (X) and output (Y) variables X = dataset[:,0:8] Y = dataset[:,8] # create model model = Sequential() model.add(Dense(12, input_dim=8, activation=‘relu’)) model.add(Dense(8, activation=‘relu’)) model.add(Dense(1, activation=‘sigmoid’)) # Compile model model.compile(loss=‘binary_crossentropy’, optimizer=‘adam’, metrics=[‘accuracy’]) # Fit the model model.fit(X, Y, epochs=150, batch_size=10, verbose=0) # evaluate the model scores = model.evaluate(X, Y, verbose=0) print(“%s: %.2f%%” % (model.metrics_names[1], scores[1]*100))
# serialize model to JSON model_json = model.to_json() with open(“model.json”, “w”) as json_file: json_file.write(model_json) # serialize weights to HDF5 model.save_weights(“model.h5”) print(“Saved model to disk”)
# later…
# load json and create model json_file = open(‘model.json’, ‘r’) loaded_model_json = json_file.read() json_file.close() loaded_model = model_from_json(loaded_model_json) # load weights into new model loaded_model.load_weights(“model.h5”) print(“Loaded model from disk”)
# evaluate loaded model on test data loaded_model.compile(loss=‘binary_crossentropy’, optimizer=‘rmsprop’, metrics=[‘accuracy’]) score = loaded_model.evaluate(X, Y, verbose=0) print(“%s: %.2f%%” % (loaded_model.metrics_names[1], score[1]*100)) |
Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.
Running this example provides the output below.
acc: 78.78% Saved model to disk Loaded model from disk acc: 78.78% |
The JSON format of the model looks like the following:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | { “class_name”:”Sequential”, “config”:{ “name”:”sequential_1″, “layers”:[ { “class_name”:”Dense”, “config”:{ “name”:”dense_1″, “trainable”:true, “batch_input_shape”:[ null, 8 ], “dtype”:”float32″, “units”:12, “activation”:”relu”, “use_bias”:true, “kernel_initializer”:{ “class_name”:”VarianceScaling”, “config”:{ “scale”:1.0, “mode”:”fan_avg”, “distribution”:”uniform”, “seed”:null } }, “bias_initializer”:{ “class_name”:”Zeros”, “config”:{
} }, “kernel_regularizer”:null, “bias_regularizer”:null, “activity_regularizer”:null, “kernel_constraint”:null, “bias_constraint”:null } }, { “class_name”:”Dense”, “config”:{ “name”:”dense_2″, “trainable”:true, “dtype”:”float32″, “units”:8, “activation”:”relu”, “use_bias”:true, “kernel_initializer”:{ “class_name”:”VarianceScaling”, “config”:{ “scale”:1.0, “mode”:”fan_avg”, “distribution”:”uniform”, “seed”:null } }, “bias_initializer”:{ “class_name”:”Zeros”, “config”:{
} }, “kernel_regularizer”:null, “bias_regularizer”:null, “activity_regularizer”:null, “kernel_constraint”:null, “bias_constraint”:null } }, { “class_name”:”Dense”, “config”:{ “name”:”dense_3″, “trainable”:true, “dtype”:”float32″, “units”:1, “activation”:”sigmoid”, “use_bias”:true, “kernel_initializer”:{ “class_name”:”VarianceScaling”, “config”:{ “scale”:1.0, “mode”:”fan_avg”, “distribution”:”uniform”, “seed”:null } }, “bias_initializer”:{ “class_name”:”Zeros”, “config”:{
} }, “kernel_regularizer”:null, “bias_regularizer”:null, “activity_regularizer”:null, “kernel_constraint”:null, “bias_constraint”:null } } ] }, “keras_version”:”2.2.5″, “backend”:”tensorflow” } |
Save Your Neural Network Model to YAML
Note: This method only applies to TensorFlow 2.5 or earlier. If you run it in later versions of TensorFlow, you will see a RuntimeError with the message “Method model.to_yaml() has been removed due to security risk of arbitrary code execution. Please use model.to_json() instead.”
This example is much the same as the above JSON example, except the YAML format is used for the model specification.
Note, this example assumes that you have PyYAML 5 installed, for example:
In this example, the model is described using YAML, saved to file model.yaml and later loaded into a new model via the model_from_yaml() function.
Weights are handled in the same way as above in HDF5 format as model.h5.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | # MLP for Pima Indians Dataset serialize to YAML and HDF5 from tensorflow.keras.models import Sequential, model_from_yaml from tensorflow.keras.layers import Dense import numpy import os # fix random seed for reproducibility seed = 7 numpy.random.seed(seed) # load pima indians dataset dataset = numpy.loadtxt(“pima-indians-diabetes.csv”, delimiter=“,”) # split into input (X) and output (Y) variables X = dataset[:,0:8] Y = dataset[:,8] # create model model = Sequential() model.add(Dense(12, input_dim=8, activation=‘relu’)) model.add(Dense(8, activation=‘relu’)) model.add(Dense(1, activation=‘sigmoid’)) # Compile model model.compile(loss=‘binary_crossentropy’, optimizer=‘adam’, metrics=[‘accuracy’]) # Fit the model model.fit(X, Y, epochs=150, batch_size=10, verbose=0) # evaluate the model scores = model.evaluate(X, Y, verbose=0) print(“%s: %.2f%%” % (model.metrics_names[1], scores[1]*100))
# serialize model to YAML model_yaml = model.to_yaml() with open(“model.yaml”, “w”) as yaml_file: yaml_file.write(model_yaml) # serialize weights to HDF5 model.save_weights(“model.h5”) print(“Saved model to disk”)
# later…
# load YAML and create model yaml_file = open(‘model.yaml’, ‘r’) loaded_model_yaml = yaml_file.read() yaml_file.close() loaded_model = model_from_yaml(loaded_model_yaml) # load weights into new model loaded_model.load_weights(“model.h5”) print(“Loaded model from disk”)
# evaluate loaded model on test data loaded_model.compile(loss=‘binary_crossentropy’, optimizer=‘rmsprop’, metrics=[‘accuracy’]) score = loaded_model.evaluate(X, Y, verbose=0) print(“%s: %.2f%%” % (loaded_model.metrics_names[1], score[1]*100)) |
Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.
Running the example displays the following output.
acc: 78.78% Saved model to disk Loaded model from disk acc: 78.78% |
The model described in YAML format looks like the following:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | backend: tensorflow class_name: Sequential config: layers: – class_name: Dense config: activation: relu activity_regularizer: null batch_input_shape: !!python/tuple – null – 8 bias_constraint: null bias_initializer: class_name: Zeros config: {} bias_regularizer: null dtype: float32 kernel_constraint: null kernel_initializer: class_name: VarianceScaling config: distribution: uniform mode: fan_avg scale: 1.0 seed: null kernel_regularizer: null name: dense_1 trainable: true units: 12 use_bias: true – class_name: Dense config: activation: relu activity_regularizer: null bias_constraint: null bias_initializer: class_name: Zeros config: {} bias_regularizer: null dtype: float32 kernel_constraint: null kernel_initializer: class_name: VarianceScaling config: distribution: uniform mode: fan_avg scale: 1.0 seed: null kernel_regularizer: null name: dense_2 trainable: true units: 8 use_bias: true – class_name: Dense config: activation: sigmoid activity_regularizer: null bias_constraint: null bias_initializer: class_name: Zeros config: {} bias_regularizer: null dtype: float32 kernel_constraint: null kernel_initializer: class_name: VarianceScaling config: distribution: uniform mode: fan_avg scale: 1.0 seed: null kernel_regularizer: null name: dense_3 trainable: true units: 1 use_bias: true name: sequential_1 keras_version: 2.2.5 |
Save Model Weights and Architecture Together
Keras also supports a simpler interface to save both the model weights and model architecture together into a single H5 file.
Saving the model in this way includes everything we need to know about the model, including:
- Model weights.
- Model architecture.
- Model compilation details (loss and metrics).
- Model optimizer state.
This means that we can load and use the model directly, without having to re-compile it as we did in the examples above.
Note: this is the preferred way for saving and loading your Keras model.
How to Save a Keras Model
You can save your model by calling the save() function on the model and specifying the filename.
The example below demonstrates this by first fitting a model, evaluating it and saving it to the file model.h5.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | # MLP for Pima Indians Dataset saved to single file from numpy import loadtxt from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense # load pima indians dataset dataset = loadtxt(“pima-indians-diabetes.csv”, delimiter=“,”) # split into input (X) and output (Y) variables X = dataset[:,0:8] Y = dataset[:,8] # define model model = Sequential() model.add(Dense(12, input_dim=8, activation=‘relu’)) model.add(Dense(8, activation=‘relu’)) model.add(Dense(1, activation=‘sigmoid’)) # compile model model.compile(loss=‘binary_crossentropy’, optimizer=‘adam’, metrics=[‘accuracy’]) # Fit the model model.fit(X, Y, epochs=150, batch_size=10, verbose=0) # evaluate the model scores = model.evaluate(X, Y, verbose=0) print(“%s: %.2f%%” % (model.metrics_names[1], scores[1]*100)) # save model and architecture to single file model.save(“model.h5”) print(“Saved model to disk”) |
Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.
Running the example fits the model, summarizes the models performance on the training dataset and saves the model to file.
acc: 77.73% Saved model to disk |
We can later load this model from file and use it.
Note that in Keras library, there is another function doing the same, as follows:
... # equivalent to: model.save(“model.h5”) from tensorflow.keras.models import save_model save_model(model, “model.h5”) |
How to Load a Keras Model
Your saved model can then be loaded later by calling the load_model() function and passing the filename. The function returns the model with the same architecture and weights.
In this case, we load the model, summarize the architecture and evaluate it on the same dataset to confirm the weights and architecture are the same.
# load and evaluate a saved model from numpy import loadtxt from tensorflow.keras.models import load_model
# load model model = load_model(‘model.h5’) # summarize model. model.summary() # load dataset dataset = loadtxt(“pima-indians-diabetes.csv”, delimiter=“,”) # split into input (X) and output (Y) variables X = dataset[:,0:8] Y = dataset[:,8] # evaluate the model score = model.evaluate(X, Y, verbose=0) print(“%s: %.2f%%” % (model.metrics_names[1], score[1]*100)) |
Running the example first loads the model, prints a summary of the model architecture then evaluates the loaded model on the same dataset.
Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.
The model achieves the same accuracy score which in this case is 77%.
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_1 (Dense) (None, 12) 108 _________________________________________________________________ dense_2 (Dense) (None, 8) 104 _________________________________________________________________ dense_3 (Dense) (None, 1) 9 ================================================================= Total params: 221 Trainable params: 221 Non-trainable params: 0 _________________________________________________________________
acc: 77.73% |
Protocol Buffer Format
While saving and loading a Keras model using HDF5 format is the recommended way, TensorFlow supports yet another format, the protocol buffer. It is considered faster to save and load a protocol buffer format but doing so will produce multiple files. The syntax is the same, except that we do not need to provide the .h5 extension to the filename:
# save model and architecture to single file model.save(“model”)
# … later
# load model model = load_model(‘model’) # print summary model.summary() |
These will create a directory “model” with the following files:
model/ |– assets/ |– keras_metadata.pb |– saved_model.pb `– variables/ |– variables.data-00000-of-00001 `– variables.index |
This is also the format we used to save a model in TensorFlow v1.x. You may encounter this when you download a pretrained model from TensorFlow Hub.
Further Reading
Summary
In this post, you discovered how to serialize your Keras deep learning models.
You learned how you can save your trained models to files and later load them up and use them to make predictions.
You also learned that model weights are easily stored using HDF5 format and that the network structure can be saved in either JSON or YAML format.
Do you have any questions about saving your deep learning models or about this post?
Ask your questions in the comments and I will do my best to answer them.
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