use part of Keras Sequential model to predict

Question

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 )

Answer 1

训练模型后，您可以从训练模型中检索编码器，如下所示：

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

Answer 2

I spend a bit time on Gaussian model for comapres original image and I think it is one identity you are trying to find the scopes !

You may doing with ( 1 ), return queue !

model = models.Sequential()
for layer in mainQ_outputs: 
    model.add(layer)        
    model.add(tf.keras.layers.Flatten() )
    model.add(tf.keras.layers.Dense(6, activation=tf.nn.softmax))

You also doing with ( 2 ), callbacks !

class custom_callback(tf.keras.callbacks.Callback):
    log_write_dir = log_dir
    val_dir = os.path.join(log_dir, 'validation')
    print('val_dir: ' + val_dir) # F:\models\weights\characters\minds\validation
    tf.summary.create_file_writer(val_dir)
    
    def _val_writer(self):
        if 'val' not in self._writers:
            self._writers['val'] = tf.summary.create_file_writer(val_dir)
        return self._writers['val']
    
    def on_epoch_end(self, epoch, logs={}):
        print(self.model.inputs) # [<KerasTensor: shape=(None, 1, 32, 32, 3) dtype=float32 (created by layer 'input_1')>]
        feature_extractor = tf.keras.Model(inputs=self.model.inputs, outputs=[layer.output for layer in self.model.layers], )
        x = tf.ones((32, 32, 3))
        print(np.asarray(feature_extractor))
        # [<KerasTensor: shape=(None, 1, 32, 32, 3) dtype=float32 (created by layer 'input_1')>]
        # <keras.engine.functional.Functional object at 0x000001450EFC4670>
        input('Press AnyKey!')

custom_callback = custom_callback()

** You can do with ( 3 ), layer access or references !**

group_1_ShoryuKen_Left = tf.reshape(group_1_ShoryuKen_Left, [1, 1, 48])
predictions = model.predict(group_1_ShoryuKen_Left)

layer_1 = model.get_layer( name="LSTM_32" )
# layer_1 = model.get_layer( name="Dense_64" )  # , index=None
# <keras.layers.core.dense.Dense object at 0x0000023959FB24C0>
print(layer_1)  
print(layer_1.get_weights()[0].shape)
print(layer_1.get_weights()[1].shape)

Input:

image_original = plt.imread( 'C:\\Users\\Jirayu Kaewprateep\\Pictures\\Cats\\samples\\08_resizes.jpg' )
image_original = tf.keras.preprocessing.image.img_to_array(image_original)
image_original = tf.expand_dims(image_original, 0)

image = tfa.image.gaussian_filter2d( image_original, (3, 3), 1.0, 'REFLECT', 0, name='gaussian_filter2d' )
history=conv_ae.fit(image,image_original,epochs=5)
sample_train = conv_ae.predict(image)
N_bin=50

F= conv_encoder.predict( sample_train )
print(F)

Output:

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #
=================================================================
 conv2d (Conv2D)             (None, 184, 136, 256)     7168

 batch_normalization (BatchN  (None, 184, 136, 256)    1024
 ormalization)

 conv2d_1 (Conv2D)           (None, 184, 136, 128)     295040

 max_pooling2d (MaxPooling2D  (None, 92, 68, 128)      0
 )

 conv2d_2 (Conv2D)           (None, 92, 68, 64)        73792

 batch_normalization_1 (Batc  (None, 92, 68, 64)       256
 hNormalization)

 max_pooling2d_1 (MaxPooling  (None, 46, 34, 64)       0
 2D)

=================================================================
Total params: 377,280
Trainable params: 376,640
Non-trainable params: 640
_________________________________________________________________
Model: "sequential_1"
_________________________________________________________________
 Layer (type)                Output Shape              Param #
=================================================================
 conv2d_transpose (Conv2DTra  (None, 92, 68, 128)      73856
 nspose)

 batch_normalization_2 (Batc  (None, 92, 68, 128)      512
 hNormalization)

 conv2d_transpose_1 (Conv2DT  (None, 184, 136, 256)    295168
 ranspose)

 batch_normalization_3 (Batc  (None, 184, 136, 256)    1024
 hNormalization)

 conv2d_transpose_2 (Conv2DT  (None, 184, 136, 3)      6915
 ranspose)

=================================================================
Total params: 377,475
Trainable params: 376,707
Non-trainable params: 768
_________________________________________________________________
rounded_accuracy:
<function rounded_accuracy at 0x0000027BDFF65160>
New CAE model created
Epoch 1/5
2022-03-09 14:03:44.440068: I tensorflow/stream_executor/cuda/cuda_dnn.cc:368] Loaded cuDNN version 8100
1/1 [==============================] - 3s 3s/step - loss: 0.1221 - accuracy: 0.3148
Epoch 2/5
1/1 [==============================] - 0s 31ms/step - loss: 0.0645 - accuracy: 0.3573
Epoch 3/5
1/1 [==============================] - 0s 31ms/step - loss: 0.0512 - accuracy: 0.4318
Epoch 4/5
1/1 [==============================] - 0s 31ms/step - loss: 0.0439 - accuracy: 0.4564
Epoch 5/5
1/1 [==============================] - 0s 31ms/step - loss: 0.0385 - accuracy: 0.4731
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