Row 4302
Content Data
This page contains data entry 4302 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.
def compile(self) -> Tuple\[tf.keras.Model, Callable, List\[str\], Tuple\]:
"""
Compile all the sub-objectives into one and return the objects
for the optimisation process.
​
Returns
\-------
model\_reconfigured
Model with the outputs needed for the optimization.
objective\_function
Function to call that compute the loss for the objectives.
names
Names of each objectives.
input\_shape
Shape of the input, one sample for each optimization.
"""
\# the number of inputs will be the number of combinations possible
\# of the objectives, the mask are used to take into account
\# these combinations
nb\_sub\_objectives = len(self.multipliers)
​
\# re-arrange to match the different objectives with the model outputs
masks = np.array(\[np.array(m, dtype=object) for m in itertools.product(\*self.masks)\])
masks = \[tf.cast(tf.stack(list(masks\[:, i\])), tf.float32) for i in
range(nb\_sub\_objectives)\]
​
\# the name of each combination is the concatenation of each objectives
names = np.array(\[' & '.join(names) for names in
itertools.product(\*self.names)\])
\# one multiplier by sub-objective
multipliers = tf.constant(self.multipliers)
​
def objective\_function(model\_outputs):
loss = 0.0
for output\_index in range(0, nb\_sub\_objectives):
outputs = model\_outputs\[output\_index\]
loss += self.funcs\[output\_index\](
outputs, tf.cast(masks\[output\_index\], outputs.dtype))
loss \*= multipliers\[output\_index\]
return loss
​
\# the model outputs will be composed of the layers needed
model\_reconfigured = tf.keras.Model(self.model.input, \[\*self.layers\])
​
nb\_combinations = masks\[0\].shape\[0\]
input\_shape = (nb\_combinations, \*model\_reconfigured.input.shape\[1:\])
​
return model\_reconfigured, objective\_function, names, input\_shape
someone pls help me writing this function
| Field | Value |
|---|---|
| text | def compile(self) -> Tuple\[tf.keras.Model, Callable, List\[str\], Tuple\]: """ Compile all the sub-objectives into one and return the objects for the optimisation process. ​ Returns \------- model\_reconfigured Model with the outputs needed for the optimization. objective\_function Function to call that compute the loss for the objectives. names Names of each objectives. input\_shape Shape of the input, one sample for each optimization. """ \# the number of inputs will be… |
| label | r/pytorch |
| dataType | post |
| communityName | r/pytorch |
| datetime | 2024-04-07 |
| username_encoded | Z0FBQUFBQm5LakwxVjBXRzRhNjVRN3NMcElfTUdhcFZpMU5mcDA2OHB5aExFbVhyOV9SeDlsMUZTUUh5UENzRUhNTXh2VEdJejdaY0lSVXQtYjd6bTJjZlVYZ1VMNnF4eWc9PQ== |
| url_encoded | Z0FBQUFBQm5Lak9GQjVHNG1NQlZmcEhIa3hqMEZmQ29rdEczTGNNX3g5OFUtR3Uzd2lLRlZmRTB1M1lmaGp6WWl5SjJvOVgtTnVCRnhmZXhIbk9vX2l1WEFKM0t5el9uUElPMDI3czVJanl4WnptS2FsNV9adjIyenEwLTc2MWk4QU1QRGZ6SDM4VTZiS0hwbS1tUjk0azhKNGh4M25Eang0UXlRZW81WFVZdUxPSm9GVjdNRndMZTVSN1ctUTd1TG80YlNLR2FXc2lPaExRYkE0UGdwVjJ0cFl4NjdZS3hEUT09 |
Raw Record
{
"text": "def compile(self) -> Tuple\\[tf.keras.Model, Callable, List\\[str\\], Tuple\\]:\n\n\"\"\"\n\nCompile all the sub-objectives into one and return the objects\n\nfor the optimisation process.\n\n​\n\nReturns\n\n\\-------\n\nmodel\\_reconfigured\n\nModel with the outputs needed for the optimization.\n\nobjective\\_function\n\nFunction to call that compute the loss for the objectives.\n\nnames\n\nNames of each objectives.\n\ninput\\_shape\n\nShape of the input, one sample for each optimization.\n\n\"\"\"\n\n\\# the number of inputs will be the number of combinations possible\n\n\\# of the objectives, the mask are used to take into account\n\n\\# these combinations\n\nnb\\_sub\\_objectives = len(self.multipliers)\n\n​\n\n\\# re-arrange to match the different objectives with the model outputs\n\nmasks = np.array(\\[np.array(m, dtype=object) for m in itertools.product(\\*self.masks)\\])\n\nmasks = \\[tf.cast(tf.stack(list(masks\\[:, i\\])), tf.float32) for i in\n\nrange(nb\\_sub\\_objectives)\\]\n\n​\n\n\\# the name of each combination is the concatenation of each objectives\n\nnames = np.array(\\[' & '.join(names) for names in\n\nitertools.product(\\*self.names)\\])\n\n\\# one multiplier by sub-objective\n\nmultipliers = tf.constant(self.multipliers)\n\n​\n\ndef objective\\_function(model\\_outputs):\n\nloss = 0.0\n\nfor output\\_index in range(0, nb\\_sub\\_objectives):\n\noutputs = model\\_outputs\\[output\\_index\\]\n\nloss += self.funcs\\[output\\_index\\](\n\noutputs, tf.cast(masks\\[output\\_index\\], outputs.dtype))\n\nloss \\*= multipliers\\[output\\_index\\]\n\nreturn loss\n\n​\n\n\\# the model outputs will be composed of the layers needed\n\nmodel\\_reconfigured = tf.keras.Model(self.model.input, \\[\\*self.layers\\])\n\n​\n\nnb\\_combinations = masks\\[0\\].shape\\[0\\]\n\ninput\\_shape = (nb\\_combinations, \\*model\\_reconfigured.input.shape\\[1:\\])\n\n​\n\nreturn model\\_reconfigured, objective\\_function, names, input\\_shape \n\n\n \n\n\nsomeone pls help me writing this function",
"label": "r/pytorch",
"dataType": "post",
"communityName": "r/pytorch",
"datetime": "2024-04-07",
"username_encoded": "Z0FBQUFBQm5LakwxVjBXRzRhNjVRN3NMcElfTUdhcFZpMU5mcDA2OHB5aExFbVhyOV9SeDlsMUZTUUh5UENzRUhNTXh2VEdJejdaY0lSVXQtYjd6bTJjZlVYZ1VMNnF4eWc9PQ==",
"url_encoded": "Z0FBQUFBQm5Lak9GQjVHNG1NQlZmcEhIa3hqMEZmQ29rdEczTGNNX3g5OFUtR3Uzd2lLRlZmRTB1M1lmaGp6WWl5SjJvOVgtTnVCRnhmZXhIbk9vX2l1WEFKM0t5el9uUElPMDI3czVJanl4WnptS2FsNV9adjIyenEwLTc2MWk4QU1QRGZ6SDM4VTZiS0hwbS1tUjk0azhKNGh4M25Eang0UXlRZW81WFVZdUxPSm9GVjdNRndMZTVSN1ctUTd1TG80YlNLR2FXc2lPaExRYkE0UGdwVjJ0cFl4NjdZS3hEUT09"
}
Entry Information
- Entry ID: 4302
- Repository: Axioma AXP
- Dataset: arrmlet/reddit_dataset_36
- Total Entries: 100,000