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

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(Le         


        

Answer 1

load_weights only sets the weights of your network. You still need to define its architecture before calling load_weights:

def create_model():
   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'))
   return model

def train():
   model = create_model()
   sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
   model.compile(loss='binary_crossentropy', optimizer=sgd)

   checkpointer = ModelCheckpoint(filepath="/tmp/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])

def load_trained_model(weights_path):
   model = create_model()
   model.load_weights(weights_path)