我遇到了重现TF2的问题。 numpy数组的模型（每一层的偏差矩阵和权重矩阵）。为了避免混淆，为确保numpy文件的一致性，使用了带有一列的2D矩阵。这篇文章显示了我是如何做到的。
class NumpyInitializer(tf.keras.initializers.Initializer):
# custom class converting numpy arrays to tf's initializers
# used to initialize both kernel and bias
def __init__(self, array):
# convert numpy array into tensor
self.array = tf.convert_to_tensor(array.tolist())
def __call__(self, shape, dtype=None):
# return tensor
return self.array
def restore_model_from_numpy(directory):
"""
Recreate model from the numpy files.
Numpy files in the directory are ordered by layers
and bias numpy matrix comes before numpy weight matrix.
In example:
directory-
- L1B.npy //numpy bias matrix for layer 1
- L1W.npy //numpy weights matrix for layer 1
- L2B.npy //numpy bias matrix for layer 2
- L2W.npy //numpy weights matrix for layer 2
Parameters:
directory - path to the directory with numpy files
Return:
tf's model recreated from numpy files
"""
def file_iterating(directory):
"""
Iterate over directory and create
dictionary of layers number and it's structure
layers[layer_number] = [numpy_bias_matrix, numpy_weight_matrix]
"""
pathlist = Path(directory).rglob("*.npy") # list of numpy files
layers = {} # initialize dictionary
index = 0
for file in pathlist: # iterate over file in the directory
if index % 2 == 0:
layers[int(index/2)] = [] # next layer - new key in dictionary
layers[int(index/2)].append(np.load(file)) # add to dictionary bias or weight
index +=1
print(file) # optional to show list of files we deal with
return layers # return dictionary
layers = file_iterating(directory) # get dictionary with model structure
inputs = Input(shape = (np.shape(layers[0][1])[0])) # create first model input layer
x = inputs
for key, value in layers.items(): # iterate over all levers in the layers dictionary
bias_initializer = NumpyInitializer(layers[key][0][0]) # create bias initializer for key's layer
kernal_initializer = NumpyInitializer(layers[key][1]) # create weights initializer for key's layer
layer_size = np.shape(layers[key][0])[-1] # get the size of the layer
new_layer = tf.keras.layers.Dense( # initialize new Dense layer
units = layer_size,
kernel_initializer=kernal_initializer,
bias_initializer = bias_initializer,
activation="tanh")
x = new_layer(x) # stack layer at the top of the previous layer
model = tf.keras.Model(inputs, x) # create tf's model based on the stacked layers
model.compile() # compile model
return model # return compiled model
在我的目录中，我有4个numpy文件（第1层-L1和第2层-L2）：
100_5_25_1Knapsack_Layer1\100_5_25_1Knapsack\L1B.npy , shape: (1, 80)
100_5_25_1Knapsack_Layer1\100_5_25_1Knapsack\L1W.npy , shape: (100, 80)
100_5_25_1Knapsack_Layer1\100_5_25_1Knapsack\L2B.npy , shape: (1, 100)
100_5_25_1Knapsack_Layer1\100_5_25_1Knapsack\L2W.npy , shape: (80, 100)
调用该函数将导致：
m = restore_model_from_numpy(my_numpy_files_directory)
m.summary()
Model: "model_592"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_312 (InputLayer) [(None, 100)] 0
_________________________________________________________________
dense_137 (Dense) (None, 80) 8080
_________________________________________________________________
dense_138 (Dense) (None, 100) 8100
=================================================================
Total params: 16,180
Trainable params: 16,180
Non-trainable params: 0
_________________________________________________________________
我希望这篇文章对我有帮助，因为这是我的第一篇文章。
快乐编码：D
答案 0 :(得分：0)
@Filip Kubacki，谢谢您的解决方案。为了社区的利益，我在此处（答案部分）提供解决方案以从NumPy数组（即权重和偏差）文件构建Tensorflow模型
class NumpyInitializer(tf.keras.initializers.Initializer):
# custom class converting numpy arrays to tf's initializers
# used to initialize both kernel and bias
def __init__(self, array):
# convert numpy array into tensor
self.array = tf.convert_to_tensor(array.tolist())
def __call__(self, shape, dtype=None):
# return tensor
return self.array
def restore_model_from_numpy(directory):
"""
Recreate model from the numpy files.
Numpy files in the directory are ordered by layers
and bias numpy matrix comes before numpy weight matrix.
In example:
directory-
- L1B.npy //numpy bias matrix for layer 1
- L1W.npy //numpy weights matrix for layer 1
- L2B.npy //numpy bias matrix for layer 2
- L2W.npy //numpy weights matrix for layer 2
Parameters:
directory - path to the directory with numpy files
Return:
tf's model recreated from numpy files
"""
def file_iterating(directory):
"""
Iterate over directory and create
dictionary of layers number and it's structure
layers[layer_number] = [numpy_bias_matrix, numpy_weight_matrix]
"""
pathlist = Path(directory).rglob("*.npy") # list of numpy files
layers = {} # initialize dictionary
index = 0
for file in pathlist: # iterate over file in the directory
if index % 2 == 0:
layers[int(index/2)] = [] # next layer - new key in dictionary
layers[int(index/2)].append(np.load(file)) # add to dictionary bias or weight
index +=1
print(file) # optional to show list of files we deal with
return layers # return dictionary
layers = file_iterating(directory) # get dictionary with model structure
inputs = Input(shape = (np.shape(layers[0][1])[0])) # create first model input layer
x = inputs
for key, value in layers.items(): # iterate over all levers in the layers dictionary
bias_initializer = NumpyInitializer(layers[key][0][0]) # create bias initializer for key's layer
kernal_initializer = NumpyInitializer(layers[key][1]) # create weights initializer for key's layer
layer_size = np.shape(layers[key][0])[-1] # get the size of the layer
new_layer = tf.keras.layers.Dense( # initialize new Dense layer
units = layer_size,
kernel_initializer=kernal_initializer,
bias_initializer = bias_initializer,
activation="tanh")
x = new_layer(x) # stack layer at the top of the previous layer
model = tf.keras.Model(inputs, x) # create tf's model based on the stacked layers
model.compile() # compile model
return model # return compiled model
在我的目录中，我有4个numpy文件（第1层-L1和第2层-L2）：
100_5_25_1Knapsack_Layer1\100_5_25_1Knapsack\L1B.npy , shape: (1, 80)
100_5_25_1Knapsack_Layer1\100_5_25_1Knapsack\L1W.npy , shape: (100, 80)
100_5_25_1Knapsack_Layer1\100_5_25_1Knapsack\L2B.npy , shape: (1, 100)
100_5_25_1Knapsack_Layer1\100_5_25_1Knapsack\L2W.npy , shape: (80, 100)
调用该函数将导致：
m = restore_model_from_numpy(my_numpy_files_directory)
m.summary()
Model: "model_592"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_312 (InputLayer) [(None, 100)] 0
_________________________________________________________________
dense_137 (Dense) (None, 80) 8080
_________________________________________________________________
dense_138 (Dense) (None, 100) 8100
=================================================================
Total params: 16,180
Trainable params: 16,180
Non-trainable params: 0
_________________________________________________________________