valueerror

Google Colab-ValueError: Mountpoint must be in a directory that exists

拈花ヽ惹草 提交于 2019-11-30 16:06:21
问题 I want to mount google drive on google Colab and I am using this command to mount the drive from google.colab import drive drive.mount('/content/drive/') but I am getting this error ValueError Traceback (most recent call last) <ipython-input-45-9667a744255b> in <module>() 1 from google.colab import drive ----> 2 drive.mount('content/drive/') /usr/local/lib/python3.6/dist-packages/google/colab/drive.py in mount(mountpoint, force_remount) 99 raise ValueError('Mountpoint must either be a

Simultaneously melt multiple columns in Python Pandas

帅比萌擦擦* 提交于 2019-11-30 09:35:53
问题 wondering if pd.melt supports melting multiple columns. I have the below examples trying to have the value_vars as list of lists but i am getting an error: ValueError: Location based indexing can only have [labels (MUST BE IN THE INDEX), slices of labels (BOTH endpoints included! Can be slices of integers if the index is integers), listlike of labels, boolean] types Using pandas 0.23.1. df = pd.DataFrame({'City': ['Houston', 'Austin', 'Hoover'], 'State': ['Texas', 'Texas', 'Alabama'], 'Name':

ValueError: unsupported pickle protocol: 3, python2 pickle can not load the file dumped by python 3 pickle?

六眼飞鱼酱① 提交于 2019-11-28 16:27:40
I use pickle to dump a file on python 3, and I use pickle to load the file on python 2, the ValueError appears. So, python 2 pickle can not load the file dumped by python 3 pickle? If I want it? How to do? You should write the pickled data with a lower protocol number in Python 3. Python 3 introduced a new protocol with the number 3 (and uses it as default), so switch back to a value of 2 which can be read by Python 2. Check the protocol parameter in pickle.dump . Your resulting code will look like this. pickle.dump(your_object, your_file, protocol=2) There is no protocol parameter in pickle

Error when checking model input: expected lstm_1_input to have 3 dimensions, but got array with shape (339732, 29)

℡╲_俬逩灬. 提交于 2019-11-27 13:59:49
My input is simply a csv file with 339732 rows and two columns : the first being 29 feature values, i.e. X the second being a binary label value, i.e. Y I am trying to train my data on a stacked LSTM model: data_dim = 29 timesteps = 8 num_classes = 2 model = Sequential() model.add(LSTM(30, return_sequences=True, input_shape=(timesteps, data_dim))) # returns a sequence of vectors of dimension 30 model.add(LSTM(30, return_sequences=True)) # returns a sequence of vectors of dimension 30 model.add(LSTM(30)) # return a single vector of dimension 30 model.add(Dense(1, activation='softmax')) model

ValueError: unsupported pickle protocol: 3, python2 pickle can not load the file dumped by python 3 pickle?

柔情痞子 提交于 2019-11-27 09:42:56
问题 I use pickle to dump a file on python 3, and I use pickle to load the file on python 2, the ValueError appears. So, python 2 pickle can not load the file dumped by python 3 pickle? If I want it? How to do? 回答1: You should write the pickled data with a lower protocol number in Python 3. Python 3 introduced a new protocol with the number 3 (and uses it as default), so switch back to a value of 2 which can be read by Python 2. Check the protocol parameter in pickle.dump. Your resulting code will

Error when checking model input: expected lstm_1_input to have 3 dimensions, but got array with shape (339732, 29)

对着背影说爱祢 提交于 2019-11-26 18:16:23
问题 My input is simply a csv file with 339732 rows and two columns : the first being 29 feature values, i.e. X the second being a binary label value, i.e. Y I am trying to train my data on a stacked LSTM model: data_dim = 29 timesteps = 8 num_classes = 2 model = Sequential() model.add(LSTM(30, return_sequences=True, input_shape=(timesteps, data_dim))) # returns a sequence of vectors of dimension 30 model.add(LSTM(30, return_sequences=True)) # returns a sequence of vectors of dimension 30 model

sklearn error ValueError: Input contains NaN, infinity or a value too large for dtype(&#39;float64&#39;)

空扰寡人 提交于 2019-11-26 04:21:09
问题 I am using sklearn and having a problem with the affinity propagation. I have built an input matrix and I keep getting the following error. ValueError: Input contains NaN, infinity or a value too large for dtype(\'float64\'). I have run np.isnan(mat.any()) #and gets False np.isfinite(mat.all()) #and gets True I tried using mat[np.isfinite(mat) == True] = 0 to remove the infinite values but this did not work either. What can I do to get rid of the infinite values in my matrix, so that I can