load multiple models in Tensorflow : <p>I have written the following convolutional neural network (CNN) class in Tensorflow [<em>I have tried to omit some lines of code for clarity.</em>]</p>

<pre><code>class CNN:
def __init__(self,
                num_filters=16,        # initial number of convolution filters
             num_layers=5,           # number of convolution layers
             num_input=2,           # number of channels in input
             num_output=5,          # number of channels in output
             learning_rate=1e-4,    # learning rate for the optimizer
             display_step = 5000,   # displays training results every display_step epochs
             num_epoch = 10000,     # number of epochs for training
             batch_size= 64,        # batch size for mini-batch processing
             restore_file=None,      # restore file (default: None)

            ):

                # define placeholders
                self.image = tf.placeholder(tf.float32, shape = (None, None, None,self.num_input))  
                self.groundtruth = tf.placeholder(tf.float32, shape = (None, None, None,self.num_output)) 

                # builds CNN and compute prediction
                self.pred = self._build()

                # I have already created a tensorflow session and saver objects
                self.sess = tf.Session()
                self.saver = tf.train.Saver()

                # also, I have defined the loss function and optimizer as
                self.loss = self._loss_function()
                self.optimizer = tf.train.AdamOptimizer(learning_rate).minimize(self.loss)

                if restore_file is not None:
                    print("model exists...loading from the model")
                    self.saver.restore(self.sess,restore_file)
                else:
                    print("model does not exist...initializing")
                    self.sess.run(tf.initialize_all_variables())

def _build(self):
    #builds CNN

def _loss_function(self):
    # computes loss


# 
def train(self, train_x, train_y, val_x, val_y):
    # uses mini batch to minimize the loss
    self.sess.run(self.optimizer, feed_dict = {self.image:sample, self.groundtruth:gt})


    # I save the session after n=10 epochs as:
    if epoch%n==0:
        self.saver.save(sess,'snapshot',global_step = epoch)

# finally my predict function is
def predict(self, X):
    return self.sess.run(self.pred, feed_dict={self.image:X})
</code></pre>

<p>I have trained two CNNs for two separate tasks independently. Each took around 1 day. Say, model1 and model2  are saved as '<code>snapshot-model1-10000</code>' and '<code>snapshot-model2-10000</code>' (with their corresponding meta files) respectively. I can test each model and compute its performance separately.</p>

<p>Now, I want to load these two models in a single script. I would naturally try to do as below:</p>

<pre><code>cnn1 = CNN(..., restore_file='snapshot-model1-10000',..........) 
cnn2 = CNN(..., restore_file='snapshot-model2-10000',..........)
</code></pre>

<p>I encounter the error [<em>The error message is long. I just copied/pasted a snippet of it.</em>]</p>

<pre><code>NotFoundError: Tensor name "Variable_26/Adam_1" not found in checkpoint files /home/amitkrkc/codes/A549_models/snapshot-hela-95000
     [[Node: save_1/restore_slice_85 = RestoreSlice[dt=DT_FLOAT, preferred_shard=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save_1/Const_0, save_1/restore_slice_85/tensor_name, save_1/restore_slice_85/shape_and_slice)]]
</code></pre>

<p>Is there a way to load from these two files two separate CNNs? Any suggestion/comment/feedback is welcome.</p>

<p>Thank you,</p>
