I have a dataframe with 10k rows and the following columns:
array target [1,5,6,1,3,etc...] 5 [3,3,1,0,5,etc...] 10 [0,0,1,1,7,etc...] 3 . . . . . .
Each array has 33222 elements, so I have 10,000 rows each with a 33,222 long numpy.array that I want to input into a Neural Network to predict the target variable.
Here is how the NN is configured:
x = df.loc[:, 'array'] y = df.loc[:, 'target'] model = Sequential() model.add(Dense(12, activation='linear')) model.add(Dense(1, activation='linear')) model.compile(loss='mse', optimizer='adam', metrics=['accuracy', 'mse', 'mae']) model.fit(x, y, epochs=10, batch_size=1, verbose=1)
"ValueError: Please provide as model inputs either a single array or a list of arrays"
I haven't attempted to use arrays as input for a NN before, so I would also appreciate any advice on the optimal layer choice and configuration for this kind of problem.