> 98 trunc = np.asarray(trunc, dtype=dtype)ġ00 raise ValueError('Shape of sample %s of sequence at position %s ' ~\AppData\Roaming\Python\Python37\site-packages\keras_preprocessing\sequence.py in pad_sequences(sequences, maxlen, dtype, padding, truncating, value) > 158 padding=padding, truncating=truncating, value=value) ~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\preprocessing\sequence.py in pad_sequences(sequences, maxlen, dtype, padding, truncating, value)ġ57 sequences, maxlen=maxlen, dtype=dtype, > 4 input_padding = tf._sequences(inputList, maxlen = 100, dtype='float32', padding='post')ĥ stopper = tf.(monitor='loss', patience=3)Ħ history = rnnModel.fit(x=input_padding, y=labelList, batch_size = 1000, epochs = 100, verbose = 2, callbacks =, validation_split = 0.2) ValueError Traceback (most recent call last)Ģ inputList = np.asarray(train_())ģ labelList = np.asarray(train_()) The above exception was the direct cause of the following exception: TypeError: only size-1 arrays can be converted to Python scalars TypeError Traceback (most recent call last) Stopper = tf.(monitor='loss', patience=3) Input_padding = tf._sequences(, maxlen = 100, dtype='float32', padding='post') train_df2 = train_df2.sample(frac = 1).reset_index(drop = True) Train_df2 is formatted like this (shows only one row) If you use CVXPY in industry, we'd love to hear from you as well, on Discord or over email.I am just confused why the code below is giving me a value error and saying that setting a pad_sequences parameter to a sequence gives of an error when the documentation says that a sequence is indeed required. If you use CVXPY for academic work, we encourage you to cite our papers. Jaehyun Park, Enzo Busseti, AJ Friend, Judson Wilson, and Chris Dembia.įor more information about the team and our processes, see our governance document. Years includes Stephen Boyd, Eric Chu, Robin Verschueren, Michael Sommerauer, A non-exhaustive list of people who have shaped CVXPY over the TeamĬVXPY is a community project, built from the contributions of manyĬVXPY is developed and maintained by StevenĪnd Bartolomeo Stellato, with many others contributing Please get in touch with us first to make sure that your priorities align withĬontributions should be submitted as pull requests.Ī member of the CVXPY development team will review the pull request and guideīefore starting work on your contribution, please read the contributing guide. If you'd like to add a new example to our library, or implement a new feature, Browse the issue tracker, and look for issues tagged as "help wanted".Read the CVXPY source code and improve the documentation, or address TODOs.Here are some simple ways to start contributing immediately: Please be respectful in your communications with the CVXPY community, and make sure to abide by our code of conduct. To share feature requests and bug reports, use Github Issues.To have longer, in-depth discussions with the CVXPY community, use Github Discussions.To chat with the CVXPY community in real-time, join us on Discord.The CVXPY community consists of researchers, data scientists, software engineers, and students from all over the world. We welcome all kinds of issues, especially those related to correctness, documentation, performance, and feature requests.įor basic usage questions (e.g., "Why isn't my problem DCP?"), please use StackOverflow instead. We encourage you to report issues using the Github tracker. To get started with CVXPY, check out the following: InstallationĬVXPY is available on PyPI, and can be installed withįor detailed instructions, see the installation Many people, across many institutions and countries. It relies upon the open source solversĬVXPY began as a Stanford University research project. mixed-integer convex optimization problems,ĬVXPY is not a solver.# The optimal Lagrange multiplier for a constraint # is stored in constraint.dual_value. # The optimal value for x is stored in x.value. # The optimal objective is returned by prob.solve(). Import cvxpy as cp import numpy # Problem data.
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