Speak now
Please Wait Image Converting Into Text...
Embark on a journey of knowledge! Take the quiz and earn valuable credits.
Challenge yourself and boost your learning! Start the quiz now to earn credits.
Unlock your potential! Begin the quiz, answer questions, and accumulate credits along the way.
General Tech Bugs & Fixes 2 years ago
Posted on 16 Aug 2022, this text provides information on Bugs & Fixes related to General Tech. Please note that while accuracy is prioritized, the data presented might not be entirely correct or up-to-date. This information is offered for general knowledge and informational purposes only, and should not be considered as a substitute for professional advice.
Turn Your Knowledge into Earnings.
I am feeding 1080x1616 color photos into a convolutional neural network in Keras. The data is fed through the network just fine, until it gets to the last convolutional layer, throwing an error saying that it expects a 4D tensor, but instead gets a (32,1) tensor. I don't understand where this number is being generated.
This is my model architecture:
model = keras.Sequential([ Conv2D(32, (3,3), padding='same', activation='relu', data_format="channels_last", input_shape=(self.x_res,self.y_res,self.n_channels), kernel_initializer=keras.initializers.he_normal()), MaxPooling2D((2,2)), Conv2D(64, (3, 3), padding='same', activation='relu', kernel_initializer=keras.initializers.he_normal()), MaxPooling2D((2, 2)), Conv2D(128, (3, 3), padding='same', activation='relu', kernel_initializer=keras.initializers.he_normal()), MaxPooling2D((2, 2)), Conv2D(256, (3, 3), padding='same', activation='relu', kernel_initializer=keras.initializers.he_normal()), UpSampling2D((2, 2)), Conv2D(128, (3, 3), padding='same', activation='relu', kernel_initializer=keras.initializers.he_normal()), UpSampling2D((2, 2)), Conv2D(64, (3, 3), padding='same', activation='relu', kernel_initializer=keras.initializers.he_normal()), UpSampling2D((2, 2)), Conv2D(self.n_channels, (1, 1), padding='same', activation='sigmoid', kernel_initializer=keras REPLY 0 views 0 likes 0 shares Facebook Twitter Linked In WhatsApp
The input to your model should be of shape (batch_size, x_res, y_res, n_channels), please check it.
Following is a simple test:
import keras from keras.layers import Conv2D, MaxPooling2D, UpSampling2D import numpy as np x_res = 1080 y_res = 1616 n_channels = 3 model = keras.Sequential([ Conv2D(32, (3,3), padding='same', activation='relu', data_format="channels_last", input_shape=(x_res,y_res,n_channels), kernel_initializer=keras.initializers.he_normal()), MaxPooling2D((2,2)), Conv2D(64, (3, 3), padding='same', activation='relu', kernel_initializer=keras.initializers.he_normal()), MaxPooling2D((2, 2)), Conv2D(128, (3, 3), padding='same', activation='relu', kernel_initializer=keras.initializers.he_normal()), MaxPooling2D((2, 2)), Conv2D(256, (3, 3), padding='same', activation='relu', kernel_initializer=keras.initializers.he_normal()), UpSampling2D((2, 2)), Conv2D(128, (3, 3), padding='same', activation='relu', kernel_initializer=keras.initializers.he_normal()), UpSampling2D((2, 2)), Conv2D(64, (3, 3), padding='same', activation='relu', kernel_initializer=keras.initializers.he_normal()), UpSampling2D((2, 2)), Conv2D(n_channels, REPLY 0 views 0 likes 0 shares Facebook Twitter Linked In WhatsApp
No matter what stage you're at in your education or career, TuteeHub will help you reach the next level that you're aiming for. Simply,Choose a subject/topic and get started in self-paced practice sessions to improve your knowledge and scores.
General Tech 10 Answers
General Tech 7 Answers
General Tech 3 Answers
General Tech 9 Answers
General Tech 2 Answers
Ready to take your education and career to the next level? Register today and join our growing community of learners and professionals.