0
$\begingroup$

I have a pre-trained tensorflow model for image classification. It has has some convolution and maxpooling layers followed by dense layers.

I would like to split it in two pats. The first containing the convolutions/maxpooling part, the second containing the dense layers. Then I would like to feed the first part with an image, store the result into a file/variable and then using it as input for the second part.

The idea is that I could encode on the fly images with the first part, save them to disk and then using the encoded files as input. In this way I can avoid storing the original images.

I am writing an example of a whole model (based on Coursera's/deeplearning.ai "Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning")

import tensorflow as tf
mnist = tf.keras.datasets.fashion_mnist
(training_images, training_labels), (test_images, test_labels) = mnist.load_data()
training_images=training_images.reshape(60000, 28, 28, 1)
training_images=training_images / 255.0
test_images = test_images.reshape(10000, 28, 28, 1)
test_images=test_images/255.0
model = tf.keras.models.Sequential([
  tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28, 28, 1)),
  tf.keras.layers.MaxPooling2D(4, 4),
  tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
  tf.keras.layers.MaxPooling2D(2,2),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.summary()
model.fit(training_images, training_labels, epochs=5)

In the example above,

  • first_part would have the first 5 layers
  • second_part would have the last two (and possibly an input layer)

Thank you.

$\endgroup$
0
$\begingroup$

After several attemps I have the following piece of code. I created two new networks and transferred the weights from the initial network to them.

# first part of initial model
part1_model = tf.keras.models.Sequential([
  tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28, 28, 1)),
  tf.keras.layers.MaxPooling2D(4, 4),
  tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
  tf.keras.layers.MaxPooling2D(2,2),
  tf.keras.layers.Flatten()
])
part1_model.layers[0].set_weights(model.layers[0].get_weights())
part1_model.layers[1].set_weights(model.layers[1].get_weights())
part1_model.layers[2].set_weights(model.layers[2].get_weights())
part1_model.layers[3].set_weights(model.layers[3].get_weights())
part1_model.layers[4].set_weights(model.layers[4].get_weights())
part1_model.summary() 

# second part of initial model
part2_model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(128, activation='relu',input_shape=(1, 128)),
  tf.keras.layers.Dense(10, activation='softmax')
])
part2_model.summary()
part2_model.layers[0].set_weights(model.layers[5].get_weights())
part2_model.layers[1].set_weights(model.layers[6].get_weights())
# predictions holds prediction for test set from initial model
predictions = model.predict(test_images)

# predictions1 holds output of first part when test set is used as input 
predictions1 = part1_model.predict(test_images)

import numpy as np
# tmp is used to transform prediction1 in a format recognizable from part2
tmp=np.zeros((10000,1,128))
for i in range(0,10000):
    tmp[i,:,:]=predictions1[i,:]

# predictions2 holds the output of second part when the result of first part (predictions1) is used as input    
predictions2 = part2_model.predict(tmp)

# check that the result of initial model is the same with the result of the two parts
ok=0
for i in range(0,10000):
    if np.argmax(predictions[i])!=np.argmax(predictions2[i]):
        print(i," False")
    else:
        ok=ok+1
print(ok)
# also sample some cases and check probabilities
print("last entry")
print(predictions2[9999])
print(predictions[9999])
print("2nd entry")
print(predictions2[1])
print(predictions[1])
|improve this answer|||||
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.