Tensorflow keras Sequential .add is different than inline definition? - tensorflow

Question

Keras is giving different results when I define my model via the declarative method instead of the functional method. The two models appear to be equivillent, but using the ".add()" syntax works while using the declarative syntax gives errors -- it's a different error each time, but usually something like:
A target array with shape (10, 1) was passed for an output of shape (None, 16) while using as loss `mean_squared_error`. This loss expects targets to have the same shape as the output.
There seems to be something going on with auto-conversion of input shapes, but I can't tell what. Does anyone know what I'm doing wrong? Why aren't these two models exactly equivillent?
import tensorflow as tf
import tensorflow.keras
import numpy as np
x = np.arange(10).reshape((-1,1,1))
y = np.arange(10)
#This model works fine
model = tf.keras.Sequential()
model.add(tf.keras.layers.LSTM(32, input_shape=(1, 1), return_sequences = True))
model.add(tf.keras.layers.LSTM(16))
model.add(tf.keras.layers.Dense(1))
model.add(tf.keras.layers.Activation('linear'))
#This model fails. But shouldn't this be equivalent to the above?
model2 = tf.keras.Sequential(
{
tf.keras.layers.LSTM(32, input_shape=(1, 1), return_sequences = True),
tf.keras.layers.LSTM(16),
tf.keras.layers.Dense(1),
tf.keras.layers.Activation('linear')
})
#This works
model.compile(loss='mean_squared_error', optimizer='adagrad')
model.fit(x, y, epochs=1, batch_size=1, verbose=2)
#But this doesn't! Why not? The error is different each time, but usually
#something about the input size being wrong
model2.compile(loss='mean_squared_error', optimizer='adagrad')
model2.fit(x, y, epochs=1, batch_size=1, verbose=2)
Why aren't those two models equivalent? Why does one handle the input size correctly but the other doesn't? The second model fails with a different error each time (once in a while it even works) so i thought maybe there's some interaction with the first model? But I've tried commenting out the first model and that doesn't help. So why doesn't the second one work?
UPDATE: Here is the "model.summary() for the first and second model. They do seem different but I don't understand why.
For model.summary():
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm (LSTM) (None, 1, 32) 4352
_________________________________________________________________
lstm_1 (LSTM) (None, 16) 3136
_________________________________________________________________
dense (Dense) (None, 1) 17
_________________________________________________________________
activation (Activation) (None, 1) 0
=================================================================
Total params: 7,505
Trainable params: 7,505
Non-trainable params: 0
For model2.summary():
model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_2 (LSTM) (None, 1, 32) 4352
_________________________________________________________________
activation_1 (Activation) (None, 1, 32) 0
_________________________________________________________________
lstm_3 (LSTM) (None, 16) 3136
_________________________________________________________________
dense_1 (Dense) (None, 1) 17
=================================================================
Total params: 7,505
Trainable params: 7,505
Non-trainable params: 0```

Answer 1

When you are creating the model with the inline declarations, you put the layers in curly braces {}, which makes it a set, which is inherently unordered. Change the curly braces to square brackets [] to put them in an ordered list. This will make sure that the layers are in the correct order in your model.