Tensorflow nonsense reshape values - tensorflow

Question

When using tensorflow.keras.layers.Reshape I've been getting strange errors. Where is it getting the 47409408 value from? 207936 corresponds to the correct size (69312*3).
A weird aspect is if I put a flatten layer before the reshape it works.
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 304, 228, 3) 30
_________________________________________________________________
reshape (Reshape) (None, 69312, 3) 0
=================================================================
Total params: 30
Trainable params: 30
Non-trainable params: 0
____________________________________
(0) Invalid argument: Input to reshape is a tensor with 207936 values, but the requested shape has 47409408
import tensorflow as tf
import numpy as np
from sklearn.model_selection import train_test_split
from PIL import Image
from tensorflow.keras import datasets, layers, models, preprocessing
import os
from natsort import natsorted
from tensorflow.keras.models import Model
BATCH_SIZE = 32
EPOCHS = 15
LEARNING_RATE = 1e-4
#jpegs with values from 0 to 255
img_dir = ".../normalized_imgs"
# .npy files of size (69312,3)
pts_dir = ".../normalized_pts"
img_files = [os.path.join(img_dir, f)
for f in natsorted(os.listdir(img_dir))]
pts_files = [os.path.join(pts_dir, f)
for f in natsorted(os.listdir(pts_dir))]
img = Image.open(img_files[0])
pts = np.load(pts_files[0])
def parse_img_input(img_file, pts_file):
def _parse_input(img_file, pts_file):
# get image
d_filepath = img_file.numpy().decode()
d_image_decoded = tf.image.decode_jpeg(tf.io.read_file(d_filepath), channels=1)
d_image = tf.cast(d_image_decoded, tf.float32) / 255.0
# get numpy data
pts_filepath = pts_file.numpy().decode()
pts = np.load(pts_filepath, allow_pickle= True)
print("d_image ",d_image.shape )
return d_image, pts
return tf.py_function(_parse_input,
inp=[img_file, pts_file],
Tout=[tf.float32, tf.float32])
class SimpleCNN(Model):
def __init__(self):
super(SimpleCNN, self).__init__()
input_shape = (img.size[0], img.size[1], 1)
self.model = model = models.Sequential()
model.add(tf.keras.Input(shape= input_shape))
model.add(layers.Conv2D(3, (3,3), padding='same'))
model.add(layers.Reshape((pts.shape[0], pts.shape[1])))
# split input data into train, test sets
X_train_file, X_test_file, y_train_file, y_test_file = train_test_split(img_files, pts_files,
test_size=0.2,
random_state=0)
model = SimpleCNN()
dataset_train = tf.data.Dataset.from_tensor_slices((X_train_file, y_train_file))
dataset_train = dataset_train.map(parse_img_input)
dataset_test = tf.data.Dataset.from_tensor_slices((X_test_file, y_test_file))
dataset_test = dataset_test.map(parse_img_input)
model.compile(optimizer=tf.keras.optimizers.Adam(LEARNING_RATE), loss= tf.losses.MeanSquaredError(), metrics= [tf.keras.metrics.get('accuracy')])
model.fit(dataset_train, epochs=EPOCHS, shuffle=True, validation_data= dataset_test)