Bdtjtk

I am trying to create a neural network.
Here is my neural network design

    num_channels=3

    filter_size_conv1=3

    filter_size_conv2=3

    filter_size_conv3=3

    num_filters_conv1=32

    num_filters_conv2=64

    num_filters_conv3=128

    num_classes=1

    img_size=196.0

    fc_layer_size=80000

    num_channelss=3.0

    #__________________Creating the MODEL______________________



    x = tf.placeholder(tf.float32, shape=[None, img_size,img_size,num_channelss], name="x_placeholder")



    y = tf.placeholder(tf.float32, shape=[None, num_classes], name="y_true")

    y_true_cls = tf.argmax(y, axis=1)







    #neural network Design

    layer_conv1 = create_convolutional_layer(input=x,num_input_channels=num_channels,conv_filter_size=filter_size_conv1,num_filters=num_filters_conv1,name="conv1")



layer_conv1_1 = create_convolutional_layer(input=layer_conv1,num_input_channels=num_filters_conv1,conv_filter_size=filter_size_conv1,num_filters=num_filters_conv1,name="conv2")



layer_conv1_1_1 = create_convolutional_layer(input=layer_conv1_1,num_input_channels=num_filters_conv1,conv_filter_size=filter_size_conv1,num_filters=num_filters_conv1,name="conv3")



max_pool_1=maxpool2d(layer_conv1_1_1,2,name="maxpool_1")



drop_out_1=dropout(max_pool_1,name="dropout_1")



flatten_layer=create_flatten_layer(drop_out_3)



layer_fc2 = create_fc_layer(input=flatten_layer,num_inputs=fc_layer_size,num_outputs=num_classes,use_relu=True)





y_pred = tf.nn.softmax(layer_fc2,name="y_pred")



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



#Defining objective

train = tf.train.AdamOptimizer(learning_rate=0.00001).minimize(cost)





print ("_____Neural Network Architecture Created Succefully_____")

epochs=10

    matches = tf.equal(tf.argmax(y_pred,axis=1),tf.argmax(y,axis=1))

acc = tf.reduce_mean(tf.cast(matches,tf.float32))





#Initializing weights

init = tf.global_variables_initializer()



with tf.Session() as sess:

    #writing output to the logs for tensorboard 

    writer=tf.summary.FileWriter("./logs",sess.graph)

    sess.run(init)



    for i in range(epochs):

        #creating smaller batches



        for j in  range(0,steps-remaining,step_size):

            sess.run([acc,train,cost],feed_dict={x:X_train[j:j+step_size],y:y_train[j:j+step_size]})

Now my input first from X_train to the model is of the dimension (7,196,196,3).

X_train contains 22 images.

Here is the Error Trace:

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'x_placeholder' with dtype float and shape [?,196,196,3]

     [[Node: x_placeholder = Placeholder[dtype=DT_FLOAT, shape=[?,196,196,3], _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]

     [[Node: Mean/_15 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_147_Mean", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

I am not able to find the bug, I am feeding in the right dimensions,still error.