Keras fit ValueError in extremely simple test code - keras

Question

I have encountered a very persistent problem in a more complex Keras program but have boiled it down to this: The answer must be very simple but I can't find it.
When I run this code:
def __init__ (self):
self.model = Sequential()
self.model.add(Dense(4, input_shape=(4,), activation='linear'))
self.model.compile(optimizer='adam', loss='mse')
def run(self):
x = [1., 1., 1., 1.]
print('x:', x, 'x shape:', np.shape(x))
y = [0., 0., 0., 0.]
print('y:', y, 'y shape:', np.shape(y))
self.model.fit(x, y, batch_size=1, epochs=1, verbose=2)
The print statements show both x and y to be of shape (4,) but the fit line generates:
ValueError: Error when checking input: expected dense_1_input to have
shape (4,) but got array with shape (1,)
I've tried reshaping x to (1,4) but it didn't help. I'm stumped.

Answer 1

Data should be 2D.
Make your x and y data as 2D by x = [[1., 1., 1., 1.]]. It becomes 1x4 data.
1 is number of data and 4 is dimension what you define as the input_shape.
And, make it as numpy array by x = np.array(x). Keras's fit method requires numpy array. I saw x: Numpy array of training data from https://keras.io/models/model/.
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
class A:
def __init__ (self):
self.model = Sequential()
self.model.add(Dense(4, input_shape=(4,), activation='linear'))
self.model.compile(optimizer='adam', loss='mse')
def run(self):
x = [[1., 1., 1., 1.]]
print('x:', x, 'x shape:', np.shape(x))
y = [[0., 0., 0., 0.]]
print('y:', y, 'y shape:', np.shape(y))
x = np.array(x)
y = np.array(y)
self.model.fit(x, y, batch_size=1, epochs=1, verbose=2)
a = A()
a.run()

Answer 2

the x and y arrays you pass are not the right shape. If you want to have input tensor of shape (4,) for your model then you have to prepare a tensor with shape (n,4) where n is the number of examples you are providing.
import tensorflow as tf
import numpy as np
from keras.models import Model, Sequential
from keras.layers import Input, Dense
class Mymodel(tf.keras.Model):
def __init__ (self):
super(Mymodel, self).__init__()
self.model = Sequential()
self.model.add(Dense(4, input_shape=(4,), activation='linear'))
self.model.compile(optimizer='adam', loss='mse')
def run(self):
x = np.ones((1,4))
print('x:', x, 'x shape:', np.shape(x))
y = np.zeros((1,4))
print('y:', y, 'y shape:', np.shape(y))
self.model.fit(x, y, batch_size=1, epochs=1, verbose=2)
model = Mymodel()
model.run()