CODEWITHSUNDEEP
pythontensorflowcomputer-visionconv-neural-network
Atinesh
Atinesh

Reputation: 1920

How to get the output of certain layer of trained CNN model [Tensorflow]

I have a CNN model for image classification which I have trained over my dataset. The model goes something like this

Convolution 
Relu
pooling

Convolution 
Relu
Convolution 
Relu
pooling

flat

fully connected (FC1)
Relu
fully connected (FC2)
softmax

After training, I want to get the feature vectors for an image that I input to the pre-trained model i.e. I want to get the output of FC1 layer. Is there any way we can get it, I browsed the web but couldn't find anything useful any suggestions would be of great help guys.

Training script

# input
x = tf.placeholder(tf.float32, shape=[None, img_size_h, img_size_w, num_channels], name='x')
# lables
y_true     = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')
y_true_cls = tf.argmax(y_true, axis=1)

y_pred = build_model(x)     # Builds model architecture
y_pred_cls = tf.argmax(y_pred, axis=1)

cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=y_pred, labels=y_true)
cost = tf.reduce_mean(cross_entropy)

optimizer = tf.train.MomentumOptimizer(learn_rate, 0.9, use_locking=False, use_nesterov=True).minimize(cost)

accuracy  = tf.reduce_mean(tf.cast(tf.equal(y_pred_cls, y_true_cls), tf.float32))

sess = tf.Session()

sess.run(tf.global_variables_initializer())

tf_saver = tf.train.Saver()

train(num_iteration)    # Trains the network and saves the model

sess.close()

Testing script

sess = tf.Session()

tf_saver = tf.train.import_meta_graph('model/model.meta')
tf_saver.restore(sess, tf.train.latest_checkpoint('model'))

x = tf.get_default_graph().get_tensor_by_name('x:0')

y_true = tf.get_default_graph().get_tensor_by_name('y_true:0')
y_true_cls = tf.argmax(y_true, axis=1)

y_pred = tf.get_default_graph().get_tensor_by_name('y_pred:0')      # refers to FC2 in the model
y_pred_cls = tf.argmax(y_pred, axis=1)

correct_prediction = tf.equal(y_pred_cls, y_true_cls)
accuracy           = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

images, labels = read_data()     # read data for testing

feed_dict_test  = {x: images, y_true: labels}

test_acc = sess.run(accuracy, feed_dict=feed_dict_test)

sess.close()

Upvotes: 1

Views: 4428

Answers (1)

tomkot
tomkot

Reputation: 956

You can just perform sess.run on the right tensor to get the values. First you need the tensor. You can give it a name inside build_model by adding a name argument (which you can do for any tensor), e.g.:

FC1 = tf.add(tf.multiply(Flat, W1), b1, name="FullyConnected1")

Later, you can get the tensor for the fully connected layer and evaluate it:

with tf.Session() as sess:
    FC1 = tf.get_default_graph().get_tensor_by_name('FullyConnected1:0')
    FC1_values = sess.run(FC1, feed_dict={x: input_img_arr})

(This is assuming there is no other layer called FullyConnected1 in the graph)

Upvotes: 4

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