number of parameters for convolution layers
Actually it's $49C*C$, the first $C$ is the number of input channels, and the second $C$ is the number of filters.

Quote from CS231n:


  To summarize, the Conv Layer:
  
  
  Accepts a volume of size $W_1 \times H_1 \times D_1$
  Requires four hyperparameters:
  
  
  Number of filters $K$,
  their spatial extent $F$,
  the stride $S$,
  the amount of zero padding $P$.
  
  Produces a volume of size $W_2 \times H_2 \times D_2$ where:
  
  
  $W_2 = (W_1 - F + 2P)/S + 1$
  $H_2 = (H_1 - F + 2P)/S + 1$ (i.e. width and height are computed equally by symmetry)
  $D_2 = K$
  
  With parameter sharing, it introduces $F \cdot F \cdot D_1$ weights per filter, for a total of $(F \cdot F \cdot D_1) \cdot K$ weights and $K$ biases.
  In the output volume, the $d$-th depth slice (of size $W_2 \times H_2$) is the result of performing a valid convolution of the $d$-th filter over the input volume with a stride of $S$, and then offset by $d$-th bias.
  
  
  A common setting of the hyperparameters is $F = 3, S = 1, P = 1$. However, there are common conventions and rules of thumb that motivate these hyperparameters. See the ConvNet architectures section below.