Dense INPUT Layer: When do I have to specify the input shape?

Question

In the below code:

1. I get an error when I do not specify the input shape in the $1^{st}$ layer. I thought that TensorFlow can infer the shape for the dense layer. For some code I have seen, TensorFlow works without the input shape specified.

2. What is the input shape I should specify? X is (9, ). By default, am I supposed to omit the sample size for the input shape?

import numpy as np from numpy import array import pandas as pd from tensorflow.keras.layers import Activation, Dense, SimpleRNN, GRU, LSTM, Conv1D, Conv2D, MaxPool2D, RepeatVector, TimeDistributed X = [1, 2, 3, 4, 5, 6, 8, 9, 10] Y = [10, 15,20, 25, 30, 35, 45, 50, 55] sample_size=len(X) X = np.array(X).reshape(9, ) Y = np.array(Y).reshape(9, ) print(X.shape) model = Sequential() #model.add(Dense(100,input_shape=[1])) model.add(Dense(100)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(X, Y, epochs=500, batch_size=sample_size, verbose=1) ```

Answer 1

1. My TensorFlow 2.6 can infer the input_shape in .fit().
2. Yes, omit the sample size, (1, ) is your input_shape.

P.S. if you do not specify your input_shape, you cannot call .summary() before .fit()