title: ValueError: Input 0 is incompatible with layer conv1d_1: expected ndim=3, found ndim=4
body: <p>I am building a prediction model for the sequence data using conv1d layer provided by Keras. This is how I did</p>
  
  <pre><code>model= Sequential()
  model.add(Conv1D(60,32, strides=1, activation='relu',padding='causal',input_shape=(None,64,1)))
  model.add(Conv1D(80,10, strides=1, activation='relu',padding='causal'))
  model.add(Dropout(0.25))
  model.add(Conv1D(100,5, strides=1, activation='relu',padding='causal'))
  model.add(MaxPooling1D(1))
  model.add(Dropout(0.25))
  model.add(Dense(300,activation='relu'))
  model.add(Dense(1,activation='relu'))
  print(model.summary())
  </code></pre>
  
  <p>However, the debugging information has</p>
  
  <pre><code>Traceback (most recent call last):
  File "processing_2a_1.py", line 96, in &lt;module&gt;
  model.add(Conv1D(60,32, strides=1, activation='relu',padding='causal',input_shape=(None,64,1)))
  File "build/bdist.linux-x86_64/egg/keras/models.py", line 442, in add
  File "build/bdist.linux-x86_64/egg/keras/engine/topology.py", line 558, in __call__
  File "build/bdist.linux-x86_64/egg/keras/engine/topology.py", line 457, in assert_input_compatibility
  ValueError: Input 0 is incompatible with layer conv1d_1: expected ndim=3, found ndim=4
  </code></pre>
  
  <p>The training data and validation data shape are as follows</p>
  
  <pre><code>('X_train shape ', (1496000, 64, 1))
  ('Y_train shape ', (1496000, 1))
  ('X_val shape ', (374000, 64, 1))
  ('Y_val shape ', (374000, 1))
  </code></pre>
  
  <p>I think the <code>input_shape</code> in the first layer was not setup right. How to set it up?</p>
  
  <hr>
  
  <p><strong>Update</strong>: After using <code>input_shape=(64,1)</code>, I got the following error message, even though the model summary runs through</p>
  
  <pre><code>________________________________________________________________
  Layer (type)                 Output Shape              Param #
  =================================================================
  conv1d_1 (Conv1D)            (None, 64, 60)            1980
  _________________________________________________________________
  conv1d_2 (Conv1D)            (None, 64, 80)            48080
  _________________________________________________________________
  dropout_1 (Dropout)          (None, 64, 80)            0
  _________________________________________________________________
  conv1d_3 (Conv1D)            (None, 64, 100)           40100
  _________________________________________________________________
  max_pooling1d_1 (MaxPooling1 (None, 64, 100)           0
  _________________________________________________________________
  dropout_2 (Dropout)          (None, 64, 100)           0
  _________________________________________________________________
  dense_1 (Dense)              (None, 64, 300)           30300
  _________________________________________________________________
  dense_2 (Dense)              (None, 64, 1)             301
  =================================================================
  Total params: 120,761
  Trainable params: 120,761
  Non-trainable params: 0
  _________________________________________________________________
  None
  Traceback (most recent call last):
    File "processing_2a_1.py", line 125, in &lt;module&gt;
      history=model.fit(X_train, Y_train, batch_size=batch_size, validation_data=(X_val,Y_val), epochs=nr_of_epochs,verbose=2)
    File "build/bdist.linux-x86_64/egg/keras/models.py", line 871, in fit
    File "build/bdist.linux-x86_64/egg/keras/engine/training.py", line 1524, in fit
    File "build/bdist.linux-x86_64/egg/keras/engine/training.py", line 1382, in _standardize_user_data
    File "build/bdist.linux-x86_64/egg/keras/engine/training.py", line 132, in _standardize_input_data
  ValueError: Error when checking target: expected dense_2 to have 3 dimensions, but got array with shape (1496000, 1)
  </code></pre>