Trouble Settign GRU Input Sizes in Keras Model Stack - tensorflow

Question

I'm very new to machine learning and Keras. I'm attempting predictive values on a time series with 9 input features and 1 output feature.
I have input data of shape:(5787, 9) --> 5787 data points, 9 features per sample
I have target data of shape:(5787,) --> 5787 target data points
My model looks like this and compiles just fine:
# Build Model
model = Sequential()
model.add(layers.Dense(units=(features * neuron_per_feature),
# input_shape=input_data.shape,
activation = 'relu',
name='dense1',
))
# First GRU Layer
# input shape should be form (batch, window_size, features)
model.add(layers.GRU(features * neuron_per_feature,
# input_shape=(5787, features * neuron_per_feature),
dropout=dropout,
return_sequences=True,
name='gru1',
))
model.add(layers.Dense(features * (neuron_per_feature / 2),
activation = 'relu',
name='dense2',
))
model.add(layers.GRU(features * 4,
# input_shape=(window_size * features, ),
dropout=dropout,
return_sequences=False,
name='gru2',
))
model.add(layers.Dense(1,
name='dense3',
))
# Configure Adam optimizer
opt = keras.optimizers.Adam(
learning_rate=learning_rate,
beta_1=0.9,
beta_2=0.98,
epsilon=1e-9)
# Compile Model
model.compile(optimizer=opt, loss='mse') # mse = mean squared error
# model.summary()
I try to train the model like this:
`# Fit network
history = model.fit(
x=input_data,
y=target_data,
validation_split=0.0,
batch_size=batch_size,
epochs=epochs,
verbose="auto",
# validation_data=(test_X, test_Y),
shuffle=False,
workers=2,
use_multiprocessing=True,
)`
However, I continually get errors when adding in the GRU layers:
`<ipython-input-62-76971a07670d> in <module>
1 # Fit network
----> 2 history = model.fit(
3 x=input_data,
4 y=target_data,
5 validation_split=0.0,
1 frames
/usr/local/lib/python3.8/dist-packages/keras/engine/training.py in tf__train_function(iterator)
13 try:
14 do_return = True
---> 15 retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
16 except:
17 do_return = False
ValueError: in user code:
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1051, in train_function *
return step_function(self, iterator)
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1040, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1030, in run_step **
outputs = model.train_step(data)
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 889, in train_step
y_pred = self(x, training=True)
File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.8/dist-packages/keras/engine/input_spec.py", line 214, in assert_input_compatibility
raise ValueError(f'Input {input_index} of layer "{layer_name}" '
ValueError: Exception encountered when calling layer "sequential_19" (type Sequential).
Input 0 of layer "gru1" is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 108)
Call arguments received by layer "sequential_19" (type Sequential):
• inputs=tf.Tensor(shape=(None, 9), dtype=float32)
• training=True
• mask=None`
Any help would be much appreciated!
I've tried disabling the GRU layers, and the model will run the fit training. But when I re-add the GRUs, it fails.