@jpeg729 @ashwin.raju93
Sorry for late response. Here are the keras codes:
x = Input(shape=(200, 76))
h = Convolution1D(10, 9, activation = 'relu', name='conv_1')(x)
h = Convolution1D(10, 9, activation = 'relu', name='conv_2')(h)
h = Convolution1D(20, 11, activation = 'relu', name='conv_3')(h)

model = Model(x, h)
print model.summary()

And the output is
____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
input_6 (InputLayer)             (None, 200, 76)       0                                            
____________________________________________________________________________________________________
conv_1 (Convolution1D)           (None, 192, 10)       6850        input_6[0][0]                    
____________________________________________________________________________________________________
conv_2 (Convolution1D)           (None, 184, 10)       910         conv_1[0][0]                     
____________________________________________________________________________________________________
conv_3 (Convolution1D)           (None, 174, 20)       2220        conv_2[0][0]                     

And corresponding pytorch code is:
nn.Sequential(
            nn.Conv1d(in_channels=200, out_channels=192, kernel_size=10),
            nn.ReLU(),
            nn.Conv1d(in_channels=192, out_channels=184, kernel_size=10),
            nn.ReLU(),
            nn.Conv1d(in_channels=184, out_channels=174, kernel_size=20),
            nn.ReLU(),
        )

But the model structure is
(500L, 192L, 67L)
(500L, 192L, 67L)
(500L, 184L, 58L)
(500L, 184L, 58L)
(500L, 174L, 39L)
(500L, 174L, 39L)