Tensorflow: Incompatible shapes: [1,2] vs. [1,4,4,2048]

I have the following tensorflow model:

img_width, img_height = 120, 120

dg = DataGenerator('/mnt/e/Shared/Stfc/Images', target_size=(img_height, img_width), batch_size=1)

input_tensor = tf.keras.Input(shape=(img_width, img_height, 3))
base_model = tf.keras.applications.ResNet50(weights='imagenet', include_top=False, input_tensor=input_tensor)

model = base_model
optimizer = tf.keras.optimizers.RMSprop(0.001)

model.compile(loss='mse',
              optimizer=optimizer,
              metrics=['mae', 'mse'])

model.fit(dg)

I have simplified this somewhat in an attempt to narrow down the problem,

When I run this I get the following error:

tensorflow.python.framework.errors_impl.InvalidArgumentError:  Incompatible shapes: [1,2] vs. [1,4,4,2048]
         [[node mean_squared_error/SquaredDifference (defined at /projects/tensorflow/stfcxy.py:130) ]] [Op:__inference_train_function_15679]

This error always seems to occur on a different input image. All my images are exactly the same dimennsions.

I am using tensorflow 2.4.1

What am I missing?

Solution 1:

The ResNet50 model outputs a tensor with the shape (4,4,2048) and you are expecting a shape of (2,), so you will definitely have to reduce the size of that tensor by applying further dense layers. Here is a simple working example but but I would recommend using a deep network with more layers.

import tensorflow as tf

img_width, img_height = 120, 120

input_tensor = tf.keras.Input(shape=(img_width, img_height, 3))
base_model = tf.keras.applications.ResNet50(weights='imagenet', include_top=False, input_tensor=input_tensor)
x = tf.keras.layers.GlobalMaxPool2D()(base_model.output)
output = tf.keras.layers.Dense(2, activation='linear')(x)

model = tf.keras.Model(base_model.input, output)
optimizer = tf.keras.optimizers.RMSprop(0.001)

model.compile(loss='mse',
              optimizer=optimizer,
              metrics=['mae', 'mse'])

samples = 20
images = tf.random.normal((samples, 120, 120, 3))
x_y_coords = tf.random.normal((samples, 2))
model.fit(images, x_y_coords, batch_size=2, epochs=5)

Epoch 1/5
10/10 [==============================] - 20s 689ms/step - loss: 547.9037 - mae: 16.8050 - mse: 547.9037
Epoch 2/5
10/10 [==============================] - 7s 685ms/step - loss: 560.1724 - mae: 17.3702 - mse: 560.1724
Epoch 3/5
10/10 [==============================] - 7s 694ms/step - loss: 166.5985 - mae: 8.9817 - mse: 166.5985
Epoch 4/5
10/10 [==============================] - 7s 684ms/step - loss: 169.9773 - mae: 8.6677 - mse: 169.9773
Epoch 5/5
10/10 [==============================] - 7s 684ms/step - loss: 201.1059 - mae: 9.6540 - mse: 201.1059
<keras.callbacks.History at 0x7fcaae3e5890>