Save and Load Your Keras Deep Learning Model

In this tutorial, we will learn about Keras and how to use it i.e. how to create a deep learning model on Keras, how to save it, and how to load it. So, let’s first start with Keras.

Keras is basically a library for simplifying deep learning. It works with TensorFlow in the backend and was developed by Microsoft. It is easier than Tensorflow but is slower than TensorFlow. This is the reason for its use in small models, with large models TensorFlow is preferred.

How to save a model using Keras:

Importing Keras for model creation:

Use the following code for importing Keras:

from keras.models import Sequential
from keras.layers import Dense
import numpy as np

Notice that it will show “Using Tensorflow backend”, this is because Keras works on the TensorFlow backend of Python.

Creating Model:

Here, we will create a model. We will first create a model and put sequential (imported from keras.models) in it. Then we will add hidden layers to our model according to our convenience. Here, we have created three hidden layers. The first hidden layer will have 32 neurons and will take 10 inputs. In the second hidden layer, there are 10 neurons and now the activation function used is “relu”. Similarly in the last layer, there are 5 neurons and the activation function used is “sigmoid”.

In the end, the whole neural network will compile. Here, we will give optimizer i.e. used to give weight to the layers and loss which is given to minimize the cost of the network.

See the Python code below:

model = Sequential()
model.add(Dense(32, input_dim=10))
model.add(Dense(10, activation="relu"))
model.add(Dense(5, activation="sigmoid"))

Saving Model:

Here, we will save the model using command. In double quotes give the location where you want your model to be saved.'/content/drive/My Drive/Colab Notebooks/simple.h5')

How to load model using Keras:

Now, to load the previously saved model use the following code. In quotes provide the path where you have saved your model previously.

To use the model we will give the inputs for the first layer in “model.predict” function. Here, we have given 10 zeroes as input.

from keras.models import load_model
import numpy as np

model=load_model("/content/drive/My Drive/Colab Notebooks/simple.h5")

test=np.expand_dims(np.zeros(10), axis=0)



Model: "sequential_1" 
Layer (type) Output Shape Param # 
dense_1 (Dense) (None, 32) 352 
dense_2 (Dense) (None, 10) 330 
dense_3 (Dense) (None, 5) 55 
Total params: 737 
Trainable params: 737 
Non-trainable params: 0 
None [[0.5 0.5 0.5 0.5 0.5]]

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