Issue

This Content is from Stack Overflow. Question asked by tmiwetmiwtwete

Hello I have created a MobileNetV2 model i want to add layers onto it.

However, i kept getting this error:

“One of the dimensions in the output is <= 0 due to downsampling in conv2d_22. Consider increasing the input size. Received input shape [None, 3, 3, 1280] which would produce output shape with a zero or negative value in a dimension.”

this is my code so far:

base_model = tf.keras.applications.MobileNetV2(input_shape=(96,96,3),
                                               include_top=False,
                                               weights='imagenet')

model = tf.keras.Sequential()
model.add(base_model)
model.add(layers.Conv2D(60, kernel_size=(5, 5), strides=(1, 1),
                 activation='relu',
                 input_shape=(96,96,3))) #error was highlighted on this line
model.add(layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(layers.Conv2D(64, kernel_size=(5, 5), strides=(1, 1), activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(500, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))

i have tried changing the channel first and last ((96,96,3) and (3,96,96)) on the line that has problem and both produced the same error error.

Appreciate anyone who can help with this thanks.

Solution

You cannot downsample a tensor (None, 3, 3, 1280) with a kernel size of (5, 5). Think about what a 2D convolutional layer is actually doing. You could try first flattening the output of the base model and then reshaping the output with the dense and reshape layers:

import tensorflow as tf

base_model = tf.keras.applications.MobileNetV2(input_shape=(96,96,3),
                                               include_top=False,
                                               weights='imagenet')

model = tf.keras.Sequential()
model.add(base_model)
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(64 * 64 * 3)) # just an example --> choose what you want
model.add(tf.keras.layers.Reshape((64, 64, 3)))
model.add(tf.keras.layers.Conv2D(60, kernel_size=(5, 5), strides=(1, 1),
                 activation='relu',
                 input_shape=(96,96,3))) #error was highlighted on this line
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(tf.keras.layers.Conv2D(64, kernel_size=(5, 5), strides=(1, 1), activation='relu'))
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(500, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))

You also use an Upsampling layer to get the dimensions you want, but that is up to you.

This Question was asked in StackOverflow by tmiwetmiwtwete and Answered by AloneTogether It is licensed under the terms of CC BY-SA 2.5. - CC BY-SA 3.0. - CC BY-SA 4.0.