title: ValueError: Input 0 of layer conv3d_8 is incompatible with the layer: : expected min_ndim=5, found ndim=4. Full shape received: [None, 4, 150, 150]
body: <p>I'm trying to train a 3D CNN model in Keras, but i'm getting this error when I execute the cell:</p>
  <pre><code>ValueError: Input 0 of layer conv3d_8 is incompatible with the layer: : expected min_ndim=5, found ndim=4. Full shape received: [None, 4, 150, 150]
  </code></pre>
  <p>My input data is a numpy array with image data. Here are the shapes (I know that 53 is too few, but it's just for studying purposes):</p>
  <pre><code>Training data shape:  (53, 4, 150, 150)
  Training labels shape:  (53, 1)
  Validation data shape:  (14, 4, 150, 150)
  Validation labels shape:  (14, 1)
  </code></pre>
  <p>The model I'm trying to use is:</p>
  <pre><code># Create the model
  model = Sequential()
  model.add(Conv3D(32, kernel_size=(3, 3, 3), activation='relu', kernel_initializer='he_uniform', input_shape=(4,150,150)))
  model.add(MaxPooling3D(pool_size=(2, 2, 2)))
  model.add(BatchNormalization(center=True, scale=True))
  model.add(Dropout(0.5))
  model.add(Conv3D(64, kernel_size=(3, 3, 3), activation='relu', kernel_initializer='he_uniform'))
  model.add(MaxPooling3D(pool_size=(2, 2, 2)))
  model.add(BatchNormalization(center=True, scale=True))
  model.add(Dropout(0.5))
  model.add(Flatten())
  model.add(Dense(256, activation='relu', kernel_initializer='he_uniform'))
  model.add(Dense(256, activation='relu', kernel_initializer='he_uniform'))
  model.add(Dense(4, activation='softmax'))
  
  # Compile the model
  model.compile(loss='categorical_crossentropy',
                optimizer=keras.optimizers.Adam(lr=0.001),
                metrics=['accuracy'])
  model.summary()
  # Fit data to model
  history = model.fit(treino3d, treino3d_labels,
              epochs=40)
  </code></pre>
  <p>Can someone help, please?</p>
  <p>Thanks a lot!</p>