Dataset loaded successfully.
Preprocessing Done. Summary:
Images train : (837,)
Labels train : (837,)
Images test  : (91,)
Labels test  : (91,)
Unique label : [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
 24 25 26 27 28 29]
placeholder_shape [None]
WARNING:tensorflow:From train_triplets.py:25: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

WARNING:tensorflow:From /home/user/similarity/sanku/model.py:8: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.

WARNING:tensorflow:
The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
  * https://github.com/tensorflow/addons
  * https://github.com/tensorflow/io (for I/O related ops)
If you depend on functionality not listed there, please file an issue.

Traceback (most recent call last):
  File "train_triplets.py", line 30, in <module>
    anchor_output = model.conv_net(anchor_input, reuse=False)
  File "/home/user/similarity/sanku/model.py", line 11, in conv_net
    scope=scope, reuse=reuse)
  File "/home/user/.local/lib/python3.6/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 182, in func_with_args
    return func(*args, **current_args)
  File "/home/user/.local/lib/python3.6/site-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1159, in convolution2d
    conv_dims=2)
  File "/home/user/.local/lib/python3.6/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 182, in func_with_args
    return func(*args, **current_args)
  File "/home/user/.local/lib/python3.6/site-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1025, in convolution
    (conv_dims + 2, input_rank))
ValueError: Convolution expects input with rank 4, got 1

def conv_net(self, inp, reuse=False):
        vgg_model = VGG16(weights=None, include_top=False,input_shape=(32,32,3),input_tensor=inp)
        #print(" vgg_model_input {}".format(vgg_model.input))
        x = vgg_model.output
        x = GlobalAveragePooling2D()(x)
        x = Dense(4096, activation='relu')(x)
        x = Dropout(0.6)(x)
        x = Dense(4096, activation='relu')(x)
        x = Dropout(0.6)(x)
        x = Lambda(lambda  x_: K.l2_normalize(x,axis=1))(x)
        convnet_model = Model(inputs=vgg_model.input,outputs=x)
        
 
        first_conv = Conv2D(96, kernel_size=(8, 8),strides=(16,16), padding='same',name="conv_layer_1")(inp)
        first_max = MaxPool2D(pool_size=(3,3),strides = (4,4),padding='same')(first_conv)
        first_max = Flatten()(first_max)
        first_max = Lambda(lambda  x: K.l2_normalize(x,axis=1))(first_max)


        second_conv = Conv2D(96, kernel_size=(8, 8),strides=(32,32), padding='same',name="conv_layer_2")(inp)
        second_max = MaxPool2D(pool_size=(7,7),strides = (2,2),padding='same')(second_conv)
        second_max = Flatten()(second_max)
        second_max = Lambda(lambda  x: K.l2_normalize(x,axis=1))(second_max)

        merge_one = concatenate([first_max, second_max])

        merge_two = concatenate([merge_one, convnet_model.output])
        emb = Dense(512)(merge_two)
        l2_norm_final = Lambda(lambda  x: K.l2_normalize(x,axis=1))(emb)
        #emb = Flatten()(emb)
        print("Embedding {}".format(emb))
        
        return l2_norm_final