How to use batch trained model, to predict on single input? - tensorflow

Question

I have RNN model that have been trained on Dataset:
train = tf.data.Dataset.from_tensor_slices((data_x[:train_size],
data_y[:train_size])).batch(batch_size).repeat()
model:
model = tf.keras.Sequential()
model.add(tf.keras.layers.GRU(units=lstm_num_units,
return_sequences=True,
kernel_initializer='random_uniform',
recurrent_initializer='random_uniform',
bias_initializer='random_uniform',
batch_size=batch_size,
input_shape = [seq_len, num_features]))
model.add(tf.keras.layers.LSTM(units=lstm_num_units,
batch_size=batch_size,
return_sequences=True,
input_shape = [seq_len, num_features]))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(units=dence_units))
model.add(tf.keras.layers.Dropout(drop_flat))
model.add(tf.keras.layers.Dense(units=out_units))
model.add(tf.keras.layers.Softmax())
model.compile(loss="sparse_categorical_crossentropy",
optimizer=tf.train.RMSPropOptimizer(opt),
metrics=['accuracy'])
model.fit(train, epochs=EPOCHS,
steps_per_epoch=repeat_size_train,
validation_data=validate,
validation_steps=repeat_size_validate,
verbose=1,
shuffle=True)
callbacks=[tensorboard, cp_callback])
I need to do prediction on single input of seq_len, but looks like my input have to be of a batch size:
ar = np.random.randint(98, size=[batch_size, seq_len])
ar = np.reshape(ar, [batch_size, seq_len, 1])
prediction = model.m.predict(ar)
Is there a way to make it work on a single input of shape [1, seq_len, 1]?

Answer 1

Yes, simply rebuild the model without a batch size in the first layer.
Copy the weights of the old model.
newModel.set_weights(oldModel.get_weights())
The purpose of the batch size only exists in stateful=True models to keep consistency between batches.
Even though, there is no mathematical change due to batch size.