use part of Keras Sequential model to predict-CodePudding

In the following code I have defined a Sequential model, that contains two parts conv_encoder and conv_decoder. After training the model I want to use conv_encoder to predict.

How Can I access the trained conv_encoder? (See the last line of the code bellow)

And also I want to it from inside of a fucntion.

from tensorflow.keras.layers import Conv2D, Flatten ,BatchNormalization ,MaxPooling2D
from tensorflow.keras.layers import Reshape, Conv2DTranspose

# network parameters
input_shape = (200,300,3)

batch_size = 20




import tensorflow as tf

import keras
from keras.applications.vgg16 import VGG16
from skimage.feature import hog
import pandas as pd

# Defining a custom metric
def rounded_accuracy(y_true, y_pred):
    return keras.metrics.binary_accuracy(tf.round(y_true), tf.round(y_pred))


# Create the CAE model
def create_cae():

    # Define encoder
    conv_encoder = keras.models.Sequential([
        keras.layers.Conv2D(256, kernel_size=3, padding="SAME", activation="relu", input_shape=[200, 300, 3]),
        keras.layers.BatchNormalization(),
        keras.layers.Conv2D(128, kernel_size=3, padding="SAME", activation="relu"),
        keras.layers.MaxPool2D(pool_size=2),
        keras.layers.Conv2D(64, kernel_size=3, padding="SAME", activation="relu"),
        keras.layers.BatchNormalization(),
        keras.layers.MaxPool2D(pool_size=2),
 
    ])
  
   # Define decoder
    conv_decoder = keras.models.Sequential([                                     
        keras.layers.Conv2DTranspose(128, kernel_size=3, strides=2, padding="SAME", activation="relu",input_shape=[50, 75, 64]),
        keras.layers.BatchNormalization(),
        keras.layers.Conv2DTranspose(256, kernel_size=3, strides=2, padding="SAME", activation="relu"),
        keras.layers.BatchNormalization(),
        keras.layers.Conv2DTranspose(3,  kernel_size=3, strides=1, padding="SAME", activation="sigmoid"),
 ])
    
    # Define AE
    conv_ae = keras.models.Sequential([conv_encoder, conv_decoder])
    
    # Display the model's architecture
    conv_encoder.summary()
    conv_decoder.summary()
    
    # Compile the model
    conv_ae.compile(loss="mse", optimizer=keras.optimizers.Adam(),
                    metrics=[rounded_accuracy])
    
    return conv_ae

# Create CAE
conv_ae = create_cae()
print("New CAE model created") 

history=conv_ae.fit(gaussian_noise_imgs,sample_train_imgs,epochs=5)
sample_train = conv_ae.predict(gaussian_noise_imgs)
N_bin=50
F= encoder.predict( sample_train )

CodePudding user response：

After training your model, you can retrieve the encoder from the trained model like so:

encoder = keras.Model(inputs=conv_ae.layers[0].input, outputs=conv_ae.layers[0].layers[-1].output)

CodePudding user response：