Keras MaxPooling3D Layer: Negative dimension size - conv-neural-network

Question

I was just trying to design a CNN 3D network for image classification.
Here, the input shape is (?,50,50,3,1), RGB pixel data, I tried adding data_format but didn't helped me out.
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv3D(64, (3,3,3), input_shape = x_train.shape[1:], activation = tf.nn.relu))
model.add(tf.keras.layers.MaxPooling3D(pool_size=(2,2,2)))
model.add(tf.keras.layers.Conv3D(64, (3,3,3), activation = tf.nn.relu))
model.add(tf.keras.layers.MaxPooling3D(pool_size=(2,2,2)))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(64, activation = tf.nn.relu))
model.add(tf.keras.layers.Dense(1, activation = tf.nn.softmax))
model.compile(optimizer = 'adam',
loss = 'binary_crossentropy',
metrics = ['accuracy'])
model.fit(x_train, y_train, epochs = 10)
Getting This Error:
InvalidArgumentError: Negative dimension size caused by subtracting 2 from 1 for '{{node max_pooling3d/MaxPool3D}} = MaxPool3D[T=DT_FLOAT, data_format="NDHWC", ksize=[1, 2, 2, 2, 1], padding="VALID", strides=[1, 2, 2, 2, 1]](conv3d_1/Relu)' with input shapes: [?,48,48,1,64].
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-10-04d154198bb1> in <module>
2
3 model.add(tf.keras.layers.Conv3D(64, (3,3,3), input_shape = x_train.shape[1:], activation = tf.nn.relu))
----> 4 model.add(tf.keras.layers.MaxPooling3D(pool_size=(2,2,2)))
5
6 model.add(tf.keras.layers.Conv3D(64, (3,3,3), activation = tf.nn.relu))
ValueError: Negative dimension size caused by subtracting 2 from 1 for '{{node max_pooling3d/MaxPool3D}} = MaxPool3D[T=DT_FLOAT, data_format="NDHWC", ksize=[1, 2, 2, 2, 1], padding="VALID", strides=[1, 2, 2, 2, 1]](conv3d_1/Relu)' with input shapes: [?,48,48,1,64].

Answer 1

As pooling subsamples your input, there is a point where the output of the previous layer is too small to be pooled again, so depending of your input shape, remove the 2nd
model.add(tf.keras.layers.Conv3D(64, (3,3,3), activation = tf.nn.relu))
model.add(tf.keras.layers.MaxPooling3D(pool_size=(2,2,2)))
block and try it again.
If you want to get more information about pooling, I recommend this introduction:
https://machinelearningmastery.com/pooling-layers-for-convolutional-neural-networks/#:~:text=Maximum%20pooling%2C%20or%20max%20pooling,the%20case%20of%20average%20pooling.

Answer 2

I used this to change the shape of the input,
x_train = np.array(x_train).reshape(-1, 3, 50, 50, 1)
and after this I added a new parameter to the MaxPooling3D Layer,
data_format = 'channel_first'
Before this I was getting the error in 1st layer, but after this, the same error came up, but in the 2nd layer of pooling.
As #Yannick Funk told, I removed the 2nd layer and It worked fine.
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv3D(64, (3,3,3), input_shape = x_train.shape[1:], activation = tf.nn.relu))
model.add(tf.keras.layers.MaxPooling3D(pool_size=(2,2,2), data_format = 'channels_first'))
#model.add(tf.keras.layers.Conv3D(64, (3,3,3), activation = tf.nn.relu))
#model.add(tf.keras.layers.MaxPooling3D(pool_size=(2,2,2), data_format = 'channels_first'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(64, activation = tf.nn.relu))
model.add(tf.keras.layers.Dense(1, activation = tf.nn.softmax))
model.compile(optimizer = 'adam',
loss = 'binary_crossentropy',
metrics = ['accuracy'])
model.fit(x_train, y_train, epochs = 10)