title: How should I configure my Neural Network to accept a column of numpy.ndarrays as input?
body: <p>I have a dataframe with 10k rows and the following columns:</p>
  <pre><code>      array                                     target
  [1,5,6,1,3,etc...]                                5
  [3,3,1,0,5,etc...]                                10
  [0,0,1,1,7,etc...]                                3
          .                                         .
          .                                         .
          .                                         .
  </code></pre>
  <p>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.</p>
  <p>Here is how the NN is configured:</p>
  <pre><code>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)
  </code></pre>
  <p>I'm getting</p>
  <pre><code>&quot;ValueError: Please provide as model inputs either a single array or a list of arrays&quot;
  </code></pre>
  <p>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.</p>