Btukfyl

I'm trying to do the "Hello World" of Keras with Iris-Flowers and I cannot configure the input_shape. This is my error message:

"ValueError: Error when checking input: expected dense_1_input to have 3 dimensions, but got array with shape (120, 4)"

When I change my input_shape to the received input shape, it just changes what it says the received input is. As you know there are 4 inputs and one output with this data set. Does anyone know how I would configure my input_shape?

Here's my code

import tensorflow as tf



text_file = open("iris.data.txt")

rawData = text_file.read().split('n')

text_file.close()



for x in range(0,150):

    rawData[x] = rawData[x].split(',')



xs_train = 

ys_train = 

for i in range (0,40):

    ys_train.append(rawData[i][4])

    xs_train.append([rawData[i][0], rawData[i][1], rawData[i][2], rawData[i][3]])

for i in range (50,90):

    ys_train.append(rawData[i][4])

    xs_train.append([rawData[i][0], rawData[i][1], rawData[i][2], rawData[i][3]])

for i in range (100,140):

    ys_train.append(rawData[i][4])

    xs_train.append([rawData[i][0], rawData[i][1], rawData[i][2], rawData[i][3]])



xs_test = 

ys_test = 

for i in range (40,50):

    ys_test.append(rawData[i][4])

    xs_test.append([rawData[i][0], rawData[i][1], rawData[i][2], rawData[i][3]])

for i in range (90,100):

    ys_test.append(rawData[i][4])

    xs_test.append([rawData[i][0], rawData[i][1], rawData[i][2], rawData[i][3]])

for i in range (140,150):

    ys_test.append(rawData[i][4])

    xs_test.append([rawData[i][0], rawData[i][1], rawData[i][2], rawData[i][3]])



# print(xs_train)   



for i in range(0, len(ys_train)):

    if ys_train[i] == "Iris-setosa":

        ys_train[i] = [1,0,0]

    if ys_train[i] == "Iris-versicolor":

        ys_train[i] = [0,1,0]

    if ys_train[i] == "Iris-virginica":

        ys_train[i] = [0,0,1]



# print(ys_train)





model = tf.keras.models.Sequential()

model.add(tf.keras.layers.Dense(4, input_shape=(1,4), activation= 'relu'))

model.add(tf.keras.layers.Dense(3, activation=tf.nn.softmax))

model.compile(optimizer='adam',

              loss='sparse_categorical_crossentropy',

              metrics=['accuracy'])

model.fit(xs_train, ys_train, epochs=3)

My inputs are formatted so that each array is a dataset, each set including 4
data points, it goes as so:

[['5.1', '3.5', '1.4', '0.2'],

 ['4.9', '3.0', '1.4', '0.2'],

 ['4.7', '3.2', '1.3', '0.2']...]

input_shape=(4,)

Input shape doesn't care for the batch size, only for "each" sample size.