Btukfyl

My tensorflow model is defined in a class as follows:

class EENetwork:

def __init__(self, state_size, learning_rate, output_units, name='EENetwork'):

    self.learning_rate = learning_rate

    self.state_size = state_size    # State size =  height * width * cameras

    self.output_units = output_units



    with tf.variable_scope(name):

        self.inputs_ = tf.placeholder(tf.float32, [None, *state_size], name='inputs')

        self.outputs_ = tf.placeholder(tf.float32, [None, self.output_units], name='outputs_')  # To hold the training Y





        # Placeholders aren't added to the saved graph by default

        tf.add_to_collection('inputs', self.inputs_)

        tf.add_to_collection('outputs_', self.outputs_)



        # Conv1 = samples * h * w * 24

        # Assume h, w = 64, so samples * 64 * 64 * 24

        self.conv1 = tf.layers.conv2d(inputs=self.inputs_, filters=24, kernel_size=[5, 5],

                                      padding='same', activation=tf.nn.relu, name='conv1')







        # Maxpool1 = samples * h/2 * w/2 * 12 = samples * 32 * 32 * 24

        self.maxpool1 = tf.layers.max_pooling2d(inputs=self.conv1, pool_size=[2, 2], strides=2, name='mp1')



        # Conv2 = samples * 32 * 32* 36

        self.conv2 = tf.layers.conv2d(inputs=self.maxpool1, filters=36, kernel_size=[5, 5],

                                      padding='same', activation=tf.nn.relu, name='conv2')



        # Maxpool2 = samples * 16 * 16 * 36

        self.maxpool2 = tf.layers.max_pooling2d(inputs=self.conv2, pool_size=[2, 2], strides=2, name='mp2')



        # Conv3 = samples * 16 * 16 * 48

        self.conv3 = tf.layers.conv2d(inputs=self.maxpool2, filters=48, kernel_size=[3, 3],

                                      padding='same', activation=tf.nn.relu, name='conv3')



        # Maxpool3 = samples * 8 * 8 * 48

        self.maxpool3 = tf.layers.max_pooling2d(inputs=self.conv3, pool_size=[2, 2], strides=2, name='mp3')



        # Conv4 = samples * 8 * 8 * 64

        self.conv4 = tf.layers.conv2d(inputs=self.maxpool3, filters=64, kernel_size=[3, 3],

                                      padding='same', activation=tf.nn.relu, name='conv4')



        # Maxpool4 = samples * 4 * 4 * 64

        self.maxpool4 = tf.layers.max_pooling2d(inputs=self.conv4, pool_size=[2, 2], strides=2, name='mp4')



        # Flatten = samples * 1024

        self.flatten = tf.contrib.layers.flatten(self.maxpool4)



        # FC1, Output units  = 1164

        self.fc1 = tf.layers.dense(inputs=self.flatten, units=1164, activation=tf.nn.elu,

                                   kernel_initializer=tf.contrib.layers.xavier_initializer(), name='fc1')



        # FC2, Output units = 100

        self.fc2 = tf.layers.dense(inputs=self.fc1, units=100, activation=tf.nn.elu,

                                   kernel_initializer=tf.contrib.layers.xavier_initializer(), name='fc2')



        # FC3, Output units = 50

        self.fc3 = tf.layers.dense(inputs=self.fc2, units=50, activation=tf.nn.elu,

                                   kernel_initializer=tf.contrib.layers.xavier_initializer(), name='fc3')



        # FC4, Output units = 10

        self.fc4 = tf.layers.dense(inputs=self.fc3, units=10, activation=tf.nn.elu,

                                   kernel_initializer=tf.contrib.layers.xavier_initializer(), name='fc4')



        # Output 

        self.output = tf.layers.dense(inputs=self.flatten, units=self.output_units, name='DL')



        # Cost

        #self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.output, labels=self.outputs_))

        self.loss = tf.losses.mean_squared_error(labels=self.outputs_, predictions=self.output)



        # Optimizer

        self.optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss)

I am creating an instance of this class and then training and saving it. Now, after restoring the saved model in another file, I want to get the output produced by self.output.

This is the code I have written for the second file:

with tf.Session() as sess:

    saver = tf.train.import_meta_graph("./models/drivingModel_{LR}_{EP}_{MN}.ckpt.meta".format(LR=learning_rate, EP=epochs, MN=mod_number))

    saver.restore(sess, "./models/drivingModel_{LR}_{EP}_{MN}.ckpt".format(LR=learning_rate, EP=epochs, MN=mod_number))



    frontImage = cv2.imread('Front_5.png')

    leftImage = cv2.imread('Left_5.png')

    rightImage = cv2.imread('Right_5.png')

    front = cv2.resize(cv2.cvtColor(frontImage, cv2.COLOR_RGB2GRAY), (h, w))

    left = cv2.resize(cv2.cvtColor(leftImage, cv2.COLOR_RGB2GRAY), (h, w))

    right = cv2.resize(cv2.cvtColor(rightImage, cv2.COLOR_RGB2GRAY), (h, w))



    images = np.stack((front, left, right))  # num_cameras * h * w

    images = np.moveaxis(images, [0, 1, 2], [2, 0, 1])

    X = 

    X.append(images)

    X = np.array(X)  



    print(tf.get_default_graph().get_all_collection_keys())

    all_variables = tf.get_collection('variables')



    print("Variables of the tensor:")

    for var in all_variables:

    print(var)



    graph = sess.graph

    print("Operations of the tensor: ")

    operations = tf.get_collection('train_op')

    for op in graph.get_operations():

        print(op)

I want to pass the X as a feeddict parameter to self.inputs_ and get the output produced by self.output (named as 'DL').

I couldn't figure out which variables and operations can I use to perform the aforementioned task.

I can get the input placeholder using:

    inputs_ = tf.get_collection('inputs')[0]

But how can I pass X to it and use it to get the value produced at self.output layer?

Edit:
I was able to solve it by creating a new instance of the class and then running the following code:

tf.reset_default_graph()



drivingNet = cnn_model.EENetwork(state_size=state_size, 

learning_rate=learning_rate, output_units=classes)





with tf.Session() as sess:

saver = tf.train.Saver(tf.global_variables())

saver = tf.train.import_meta_graph("./models/drivingModel_{LR}_{EP}_{MN}.ckpt.meta".format(LR=learning_rate, EP=epochs, MN=mod_number))

saver.restore(sess, "./models/drivingModel_{LR}_{EP}_{MN}.ckpt".format(LR=learning_rate, EP=epochs, MN=mod_number))



frontImage = cv2.imread('Front_5.png')

leftImage = cv2.imread('Left_5.png')

rightImage = cv2.imread('Right_5.png')

front = cv2.resize(cv2.cvtColor(frontImage, cv2.COLOR_RGB2GRAY), (h, w))

left = cv2.resize(cv2.cvtColor(leftImage, cv2.COLOR_RGB2GRAY), (h, w))

right = cv2.resize(cv2.cvtColor(rightImage, cv2.COLOR_RGB2GRAY), (h, w))



images = np.stack((front, left, right))  # num_cameras * h * w

# Move the axis to make it h * w * num_cameras

images = np.moveaxis(images, [0, 1, 2], [2, 0, 1])

# images = np.concatenate((leftImage, frontImage), axis=1)

# images = np.concatenate((images, rightImage), axis=1)



# Input needs to be passed as samples * h * w * num_cameras

X = 

X.append(images)

X = np.array(X)  # This makes it 1 * h * w * cameras

decision = sess.run(drivingNet.output, feed_dict={drivingNet.inputs_: X})

temp = decision.tolist()

flat_list = [item for sublist in temp for item in sublist]

print(flat_list)

However, I still don't understand what saver = tf.train.Saver(tf.global_variables()) is doing in this case? Why is this line of code required? If I do not include this line, it throws an "attempting to use uninitialized value" error. Shouldn't the meta graph and saver.restore initialize all values to the saved values?