rm \align

chapter_generative_adversarial_networks/dcgan.md

@@ -72,14 +72,6 @@ class G_block(nn.Block):
 
 In default, the transposed convolution layer uses a $4\times 4$ kernel, $2\times 2$ strides and $1\times 1$ padding. With a input size of $16 \times 16$, it will double input's width and height.
 
-\begin{align}
-n_h^{'} \times n_w^{'} &= [(n_h k_h - (n_h-1)(k_h-s_h)- 2p_h] \times [(n_w k_w - (n_w-1)(k_w-s_w)- 2p_w]\\
-  &= [(k_h + s_h (n_h-1)- 2p_h] \times [(k_w + s_w (n_w-1)- 2p_w]\\
-  &= [(4 + 2 \times (16-1)- 2 \times 1] \times [(4 + 2 \times (16-1)- 2 \times 1]\\
-  &= 32 \times 32 .\\
-\end{align}
-
-
 ```{.python .input  n=6}
 x = np.zeros((2, 3, 16, 16))
 g_blk = G_block(20)
@@ -149,13 +141,7 @@ class D_block(nn.Block):
         return self.activation(self.batch_norm(self.conv2d(X)))
 ```
 
-A basic block with default settings will halve the width and height of the inputs. For example, given a input of shape $16 \times 16$:
-
-\begin{align}
-n_h^{'} \times n_w^{'} &= \lfloor(n_h-k_h+2p_h+s_h)/s_h\rfloor \times \lfloor(n_w-k_w+2p_w+s_w)/s_w\rfloor\\
-  &= \lfloor(16-4+2\times 1+2)/2\rfloor \times \lfloor(16-4+2\times 1+2)/2\rfloor\\
-  &= 8 \times 8 .\\
-\end{align}
+A basic block with default settings will halve the width and height of the inputs. 
 
 ```{.python .input  n=12}
 x = np.zeros((2, 3, 16, 16))