ValueError: Exception encountered when calling layer "conv2d

I aim to train my convolutional neural network to identify png images. I first convert images into tensor.

file_path = f"./STFT_spectra/STFT_spectra0.png"
image = io.read_file(file_path)
image = io.decode_png(image)
image = tf.image.convert_image_dtype(image, tf.float32)
image = tf.image.resize(image, [128,128])
print("----Here-----")
print(type(image))
print(image.shape)

Output:

----Here-----
<class 'tensorflow.python.framework.ops.EagerTensor'>
(128, 128, 4)

Then, I convert all my generated images, and save the numpy array as "image_list.p" file on the hard disk.

# total = 100
# image_list = np.empty(shape=(total, 128, 128, 4))
# for i in tqdm(range(total)):
#     file_path = f"./STFT_spectra/STFT_spectra{i}.png"
#     image = io.read_file(file_path)
#     image = io.decode_png(image)
#     image = tf.image.convert_image_dtype(image, tf.float32)
#     image = tf.image.resize(image, [128, 128])
#     image_list[i] = image

# pickle.dump(image_list, open("image_list.p", "wb"))

As for ground truth, each label is a 10 float combination, like [0.2, 0.3, 0.5, 0.6, 0.9, 0.5, 0.4, 0.6, 0.7, 0.1].

Then, I assemble the dataset:
labels = pickle.load(open(".././labels.p", "rb"))
fetched_image_list = pickle.load(open("../image_list.p", "rb"))
fetched_image_list = fetched_image_list.reshape(fetched_image_list.shape[0],
                                                fetched_image_list.shape[1],
                                                fetched_image_list.shape[2],
                                                fetched_image_list.shape[3],
                                                1)

dataset = tf.data.Dataset.from_tensor_slices((fetched_image_list, labels))

The CNN model goes like this:

model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), strides=(2,2), dilation_rate=(1,1), input_shape=(128,128,4,1), activation='relu'),
    tf.keras.layers.Conv2D(71, (3, 3), strides=(2,2), dilation_rate=(1,1), activation='relu'),
    tf.keras.layers.Conv2D(128, (3, 4), strides=(2,3), dilation_rate=(1,1),activation='relu'),
    tf.keras.layers.Conv2D(128, (3, 3), strides=(2,2), dilation_rate=(1,1),activation='relu'),
    tf.keras.layers.Conv2D(128, (3, 4), strides=(2, 3), dilation_rate=(1, 1), activation='relu'),
    tf.keras.layers.Conv2D(128, (3, 3), strides=(2, 2), dilation_rate=(1, 1), activation='relu'),
    tf.keras.layers.Dropout(0.20),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(10)
])

Everything looks fine, but problem comes,

ValueError: Exception encountered when calling layer "conv2d_1" (type Conv2D).

Negative dimension size caused by subtracting 3 from 1 for '{{node conv2d_1/Conv2D/Conv2D}} = Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1], explicit_paddings=[], padding="VALID", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true](conv2d_1/Conv2D/Reshape, conv2d_1/Conv2D/Conv2D/ReadVariableOp)' with input shapes: [?,63,1,32], [3,3,32,71].

Call arguments received by layer "conv2d_1" (type Conv2D):
  • inputs=tf.Tensor(shape=(None, 128, 63, 1, 32), dtype=float32)

How can I fix this issue? Is there any problem with the definition of CNN?

CodePudding user response：

You need to use padding with your convolutional layers. You're running out of dimension space.

Negative dimension size caused by subtracting 3 from 1 for '{{node conv2d_1/Conv2D/Conv2D}} = Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1], explicit_paddings=[], padding="VALID", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true](conv2d_1/Conv2D/Reshape, conv2d_1/Conv2D/Conv2D/ReadVariableOp)' with input shapes: [?,63,1,32], [3,3,32,71].

So add, at least to the second convolutional layer, padding="same", by default, it's "valid", which means no padding. You can't have negative dimension space.

tf.keras.layers.Conv2D(71, (3, 3), strides=(2,2), padding="same", dilation_rate=(1,1), activation='relu'),