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) For example arr = and you are suppose performing arr =, ] (shape is ) and shape of the original arr is (5,) 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.,This is happening because you are overwriting an element of an array with another element whose dimensions are not consistent with the overall array. From regression to clustering, this module has all methods which are needed.,Then, we will be making the array of two different dimensions with the data type of integer from the np.array() function. We don’t declare a data type in python, then why is this error arrises in initializing incorrect datatype?,Sklearn is a famous python library that is used to execute machine learning methods on a dataset. How Does ValueError Save Us From Incorrect Data Processing?2. We don’t declare a data type in python, then why is this error arrises in initializing incorrect datatype?,FAQs1. ValueError: setting an array element with a sequence.Ģ. Return array(a, dtype, copy=False, order=order) Np_val = np.asarray(subfeed_val, dtype=subfeed_dtype)įile "D:\Anaconda3\lib\site-packages\numpy\core\numeric.py", line 492, in asarray Python traceback: Traceback (most recent call last):įile "D:/Documents/PycharmProjects/SnakeGA/try.py", line 33, in įile "D:\Anaconda3\lib\site-packages\keras\models.py", line 845, in fitįile "D:\Anaconda3\lib\site-packages\keras\engine\training.py", line 1485, in fitįile "D:\Anaconda3\lib\site-packages\keras\engine\training.py", line 1140, in _fit_loopįile "D:\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py", line 2075, in _call_įile "D:\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 900, in runįile "D:\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1104, in _run Model.add(Dense(output_dim=1, activation='softmax'))įuture_state = random.choice()Īrray = Model.add(Dense(output_dim=50, activation='relu')) Model.add(Dense(output_dim=50, activation='relu', input_dim=4)) When I train my neural network I get Setting an array element with a sequence. The input is related to the first 4 columns, the output is the last one. I have a problem with the input of multiple data sources in my neural network. ,The input is related to the first 4 columns, the output is the last one. My dataframe is:,array is, 4, -10,, 1] in the OP implementation,and should be flattened to. With 946932 stored elements in Compressed Sparse Row format> Model.fit(X, Y, epochs=150, batch_size=10)Īnswer by Alana Why would we expect this to work in the Sequential model, but not the Functional model?,Try using InputLayer(sparse=True) in Sequential model instead of Input(sparse=True) in Functional models., pile(loss='binary_crossentropy', optimizer='adam', metrics=) Model.add(Dense(1, activation='sigmoid')) One possible solution would be to use to reshape your data, and pd.Series.tolist() to uniformize the data type of the input array: model = Sequential() The input is a mixture of arrays and lists.,One possible solution would be to use to reshape your data, and pd.Series.tolist() to uniformize the data type of the input array:,The input is the wrong shape,Software Quality Assurance & Testing
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