This is the code in question:

batch_size = 1
epochs = 1
begin_from_row = 3
rows_to_train = 1000

data = data.loc[begin_from_row:rows_to_train, :]

data['Close_next'] = data['Close'].shift(-1)
data = data.dropna()
output_data = data['Close_next']
input_data = data.drop(columns=['Close_next'])
input_size = 9
output_size = 1
hidden_size_1 = 9

input_layer = tf.keras.Input(batch_shape=(batch_size, input_size))
input_layer_expanded = tf.expand_dims(input_layer, axis=-1)
hidden_1 = tf.keras.layers.LSTM(hidden_size_1, stateful=True)(input_layer_expanded)
output_layer = tf.keras.layers.Dense(1, activation='relu')(hidden_1)

model = tf.keras.Model(inputs=input_layer, outputs=output_layer)

model.compile(loss='mean_squared_error', optimizer='adam', run_eagerly=True)

model.fit(input_data, output_data, epochs=epochs)

model.save("model_1.h5")

It returns the following error:

Input 0 of layer "lstm" is incompatible with the layer: expected shape=(1, None, 1), found shape=(32, 9, 1)

I can’t quite get where it gets the number 32 from, since it soesn’™ appear anywhere in my code

The code works when I specify the batch_size=32, just for one batch. The number 32 doesn’t appear anywhere in the code, so I would like to know where it’s coming from.

CodePudding user response：

The vector (32, 9, 1) represents the size of your input data, whereas (1, None, 1) is the expected shape that you defined in the InputLayer (batch_size = 1).

Batch_size 32 is the default value in the fit method. The default value is being used since you did not specify the batch_size argument and you are not using tensorflow datasets:

batch_size: Integer or None. Number of samples per gradient update. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of datasets, generators, or keras.utils.Sequence instances (since they generate batches).

From: Tensorflow fit function