def load_model_from_file(hdf5_model_filepath, display_summary=True):
"""
Load model from file
"""
hdf5_file = h5py.File(hdf5_model_filepath, mode='r')
model = model_pb2.Model()
model.id = str(uuid.uuid4())
model.name = 'MyModel'
model.keras_version = hdf5_file.attrs['keras_version']
model.backend = hdf5_file.attrs['backend']
model.model_config = hdf5_file.attrs['model_config']
f = hdf5_file['model_weights']
layers = {}
for layer_name in f.attrs['layer_names']:
g = f[layer_name]
layers[layer_name] = {}
for weight_name in g.attrs['weight_names']:
weight_value = g[weight_name].value
layers[layer_name][weight_name] = weight_value
hdf5_file.close()
conv_layer_weights = layers[b'conv2d'][b'conv2d/kernel:0']
conv_layer_biases = layers[b'conv2d'][b'conv2d/bias:0']
dense_layer_weights = layers[b'dense'][b'dense/kernel:0']
dense_layer_biases = layers[b'dense'][b'dense/bias:0']
pred_layer_weights = layers[b'pred'][b'pred/kernel:0']
pred_layer_biases = layers[b'pred'][b'pred/bias:0']
if (display_summary):
print("Model Summary:")
print("=========================")
print(f"Conv Layer: \nweight shape: {conv_layer_weights.shape}\nbias shape: {conv_layer_biases.shape} ")
print(f"Dense Layer: \nweight shape: {dense_layer_weights.shape}\nbias shape: {dense_layer_biases.shape} ")
print(f"Pred Layer: \nweight shape: {pred_layer_weights.shape}\nbias shape: {pred_layer_biases.shape} ")
return layers
def main():
model = mpb2.Model()
model.type = "cnn"
conv1 = model.function.add()
max_pool1 = model.function.add()
relu1 = model.function.add()
conv2 = model.function.add()
conv2_drop = model.function.add()
max_pool2 = model.function.add()
relu2 = model.function.add()
view = model.function.add()
fc1 = model.function.add()
relu3 = model.function.add()
dropout = model.function.add()
fc2 = model.function.add()
log_softmax = model.function.add()
conv1.function = "Conv2d"
conv1.init_arg.extend(["1", "10", "5"])
max_pool1.function = "max_pool2d"
max_pool1.inp.extend(["2"])
relu1.function = "relu"
conv2.function = "Conv2d"
conv2.init_arg.extend(["10", "20", "5"]);
conv2_drop.function = "Dropout2d"
conv2_drop.init_arg.extend([""])
max_pool2.function = "max_pool2d"
max_pool2.inp.extend(["2"])
relu2.function = "relu"
view.function = "view"
view.init_arg.extend(["-1", "num_flat_features"])
fc1.function = "Linear"
fc1.init_arg.extend(["num_flat_features", "50"])
relu3.function = "relu"
dropout.function = "dropout"
dropout.inp.extend(["0.5","training", "False"])
fc2.function = "Linear"
fc2.init_arg.extend(["50", "10"])
log_softmax.function = "log_softmax"
log_softmax.inp.extend(["1"])
# Write the new model to disk.
f = open(sys.argv[1], "wb+")
f.write(model.SerializeToString())
f.close()
def create_model(self):
"""Initializes a model from the protobuf definition.
"""
self.model = model_pb2.Model()
self.model.id = str(uuid.uuid4())
self.model.name = self.name
def read_proto_model(file):
model = mpb2.Model()
f = open(file, "rb")
model.ParseFromString(f.read())
return model
