Why is predict_on_batch is repeating the first output over and over?

I honestly don’t know how to describe my problem. Basically, it only uses the first values in my tables and repeats it over and over for the rest of the values in the table. Like I said, just look at the output.

Here’s my code:

import tensorflow as tf
import numpy as np

x = np.array([[1, 0, 0],     #x1
              [1, 0.5, 0],   #x2
              [1, 1, 0],   #x3
              [0.5, 1, 0],   #x4
              [0, 1, 0],     #x5
              [0, 1, 0.5],   #x6
              [0, 1, 1],   #x7
              [0, 0.5, 1],   #x8
              [0, 0, 1],     #x9
              [0.5, 0, 1],   #x10
              [1, 0, 1],   #x11
              [1, 0, 1]]) #x12
#Key:
#[red, orange, yellow, green, blue, purple]
y = np.array([[1,0,0,0,0,0],  #y1
              [0,1,0,0,0,0],  #y2
              [0,0,1,0,0,0],  #y3
              [0,0,0,1,0,0],  #y4
              [0,0,0,1,0,0],  #y5
              [0,0,0,1,0,0],  #y6
              [0,0,0,0,1,0],  #y7
              [0,0,0,0,1,0],  #y8
              [0,0,0,0,1,0],  #y9
              [0,0,0,0,0,1],  #y10
              [0,0,0,0,0,1],  #y11
              [1,0,0,0,0,0]]) #y12

# Define the model
model = tf.keras.models.Sequential()
model.add(tf.keras.Input(shape=(3)))
model.add(tf.keras.layers.Dense(10, activation=tf.keras.activations.sigmoid))
model.add(tf.keras.layers.Dense(10, activation=tf.keras.activations.sigmoid))
model.add(tf.keras.layers.Dense(10, activation=tf.keras.activations.sigmoid))
model.add(tf.keras.layers.Dense(10, activation=tf.keras.activations.sigmoid))
model.add(tf.keras.layers.Dense(10, activation=tf.keras.activations.sigmoid))
model.add(tf.keras.layers.Dense(10, activation=tf.keras.activations.sigmoid))
model.add(tf.keras.layers.Dense(10, activation=tf.keras.activations.sigmoid))
model.add(tf.keras.layers.Dense(10, activation=tf.keras.activations.sigmoid))
model.add(tf.keras.layers.Dense(10, activation=tf.keras.activations.sigmoid))
model.add(tf.keras.layers.Dense(10, activation=tf.keras.activations.sigmoid))
model.add(tf.keras.layers.Dense(6, activation=tf.keras.activations.sigmoid))

# Compile the model
model.compile(tf.keras.optimizers.Adam(learning_rate=0.1), "BinaryCrossentropy", metrics=[ 'binary_accuracy'])
model.summary()

history = model.fit(x, y, batch_size=1, epochs=500)

predictions = model.predict_on_batch(x)
print(predictions)

Here is the output:

[[0.16287932 0.07145664 0.08749434 0.26094046 0.25779992 0.16714773]
[0.16287932 0.07145664 0.08749434 0.26094046 0.25779992 0.16714773]
[0.16287932 0.07145664 0.08749434 0.26094046 0.25779992 0.16714773]
[0.16287932 0.07145664 0.08749434 0.26094046 0.25779992 0.16714773]
[0.16287932 0.07145664 0.08749434 0.26094046 0.25779992 0.16714773]
[0.16287932 0.07145664 0.08749434 0.26094046 0.25779992 0.16714773]
[0.16287932 0.07145664 0.08749434 0.26094046 0.25779992 0.16714773]
[0.16287932 0.07145664 0.08749434 0.26094046 0.25779992 0.16714773]
[0.16287932 0.07145664 0.08749434 0.26094046 0.25779992 0.16714773]
[0.16287932 0.07145664 0.08749434 0.26094046 0.25779992 0.16714773]
[0.16287932 0.07145664 0.08749434 0.26094046 0.25779992 0.16714773]
[0.16287932 0.07145664 0.08749434 0.26094046 0.25779992 0.16714773]]

Here is the link to the Replit cover page for more details:
https://replit.com/@EthanKantala/Color-Guesser-Tensorflow?v=1

I have tried adding more neurons and doing some research. I honestly have no idea what to do. Thanks for any help!