title: Keras Convolution1D input shape
body: <p>I am trying to build a simple Convolutional NN with:</p>
  
  <ul>
  <li>340 samples, </li>
  <li>260 rows per sample</li>
  <li>16 features per row.</li>
  </ul>
  
  <p>I thought the order of the shape is (batch_size, steps, input_dim), which would mean (340, 16, 260) <em>I believe</em>.</p>
  
  <p>Current code: </p>
  
  <pre><code>model = Sequential()
  model.add(Conv1D(64, kernel_size=3, activation='relu', input_shape=(340, 16, 260)))
  # model.add(Conv2D(64, 2, activation='relu'))
  model.add(MaxPooling1D())
  # model.add(Conv2D(128, 2, activation='relu'))
  model.add(Conv1D(64, kernel_size=3, activation='relu'))
  model.add(GlobalAveragePooling1D())
  model.add(Dense(1, activation='linear'))
  
  model.compile(loss='binary_crossentropy',
                optimizer='rmsprop',
                metrics=['accuracy'])
  
  model.summary()
  
  model.fit(xTrain, yTrain, batch_size=16, epochs=1000)
  </code></pre>
  
  <p>I am getting an error:</p>
  
  <blockquote>
    <p>ValueError: Input 0 is incompatible with layer conv1d_1: expected ndim=3, found ndim=4</p>
  </blockquote>
  
  <p>I am very lost and believe that my shapes are off. Could someone help me?
  Thank you!</p>