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How to do upsampling and down sampling using keras?
How to do upsampling and down sampling using keras?
This recipe helps you do upsampling and down sampling using keras
Recipe Objective
Step 1- Import Libraries
# example of using the upsampling layer
import numpy as np
from keras.models import Sequential
from keras.layers import UpSampling2D
Step 2 - Define the input array and reshape it.
We will define an input array and reshape it, to feed it to the model.
# define input data
X = np.array([10, 6, 3, 20])
# show input data for context
print(X)
# reshape input data into one sample a sample with a channel
X = X.reshape((1, 2, 2, 1))
# define model
model = Sequential()
model.add(UpSampling2D(input_shape=(2, 2, 1)))
# summarize the model
model.summary()
# make a prediction with the model
y_pred = model.predict(X)
# reshape output to remove channel to make printing easier
y_pred = y_pred.reshape((4, 4))
# summarize output
print(y_pred)
[10 6 3 20] Model: "sequential_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= up_sampling2d_1 (UpSampling2 (None, 4, 4, 1) 0 ================================================================= Total params: 0 Trainable params: 0 Non-trainable params: 0 _________________________________________________________________ [[10. 10. 6. 6.] [10. 10. 6. 6.] [ 3. 3. 20. 20.] [ 3. 3. 20. 20.]]
Step 3 - Define Sequential model
Define the model as Sequential and add UpSampling to it.
# define model
model = Sequential()
model.add(UpSampling2D(input_shape=(2, 2, 1)))
# summarize the model
model.summary()
Step 4 - Predict the model
y_pred = model.predict(X)
# reshape output to remove channel to make printing easier
y_pred = y_pred.reshape((4, 4))
# summarize output
print(y_pred)
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