Question

How to load a model from an HDF5 file in Keras?

What I tried:

model = Sequential()

model.add(Dense(64, input_dim=14, init='uniform'))

model.add(LeakyReLU(alpha=0.3))

model.add(BatchNormalization(epsilon=1e-06, mode=0, momentum=0.9, weights=None))

model.add(Dropout(0.5))

model.add(Dense(64, init='uniform'))

model.add(LeakyReLU(alpha=0.3))

model.add(BatchNormalization(epsilon=1e-06, mode=0, momentum=0.9, weights=None))

model.add(Dropout(0.5))

model.add(Dense(2, init='uniform'))

model.add(Activation('softmax'))

sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)

model.compile(loss='binary_crossentropy', optimizer=sgd)

checkpointer = ModelCheckpoint(filepath="/weights.hdf5", verbose=1, save_best_only=True)

model.fit(X_train, y_train, nb_epoch=20, batch_size=16, show_accuracy=True, validation_split=0.2, verbose = 2, callbacks=[checkpointer])

The above code successfully saves the best model to a file named weights.hdf5. What I want to do is then load that model. The below code shows how I tried to do so:

model2 = Sequential()

model2.load_weights("/Users/Desktop/SquareSpace/weights.hdf5")

This is the error I get:

IndexError: list index out of range

Answer 1

You can simply use load_model from keras.model class. 

For example:

from keras.models import load_model

model = load_model('model.h5')

This code will simply import your model from the given hdf5 file into the model variable.

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Hope this answer helps.