title: Time Series data to fit for ConvLSTM
body: <p>I used stock data with 4057 samples, made it into 28 time steps, with 25 features.</p>
  <pre><code>TrainX shape: (4057, 28, 25)
  </code></pre>
  <p>The Target consists of 5 categories of interger</p>
  <pre><code>[0,1,2,3,4]
  </code></pre>
  <p>and reshape into:</p>
  <pre><code>trainX_reshape= trainX.reshape(4057,1, 28,25,1)
  testX_reshape= testX.reshape(1334,1, 28,25,1)
  </code></pre>
  <p>trying to fit the model:</p>
  <pre><code>seq =Sequential([
      ConvLSTM2D(filters=40, kernel_size=(3, 3),input_shape=(1, 28, 25, 1),padding='same', return_sequences=True),
      BatchNormalization(),
      ConvLSTM2D(filters=40, kernel_size=(3, 3),padding='same', return_sequences=True),
      BatchNormalization(),
      ConvLSTM2D(filters=40, kernel_size=(3, 3),padding='same', return_sequences=True),
      BatchNormalization(),
      ConvLSTM2D(filters=40, kernel_size=(3, 3),padding='same', return_sequences=True),
      BatchNormalization(),
      Conv3D(filters=5, kernel_size=(3, 3, 3),activation='sigmoid',padding='same', data_format='channels_last')
  ])
  </code></pre>
  <p>compile with</p>
  <pre><code>seq.compile(loss='sparse_categorical_crossentropy', optimizer='rmsprop')
  
  history = seq.fit(trainX_reshape, trainY, epochs=10,
                 batch_size= 128, shuffle=False, verbose = 1,
                 validation_data=(testX_reshape, testY),
                 # validation_split=0.2)
  </code></pre>
  <p>and it gives ERROR:</p>
  <pre><code>InvalidArgumentError: Graph execution error:
  </code></pre>
  <p>How to fix it? Ive tried many methods, but had no clue.</p>
  <p>the code and data are at:
  <a href="https://drive.google.com/drive/folders/1WDa_CUO1Mr7wZTqE3wHsR0Tp_3NRMcZ6?usp=sharing" rel="nofollow noreferrer">https://drive.google.com/drive/folders/1WDa_CUO1Mr7wZTqE3wHsR0Tp_3NRMcZ6?usp=sharing</a></p>
  <p>works on colab</p>