How to change the input size in first layer of Resnet?

177×1 Layer array with layers: 1 '' Image Input 112×112×3 images with 'zerocenter' normalization 2 'conv1' Convolution 64 7×7×3 convolutions with stride [2 2] and padding [3 3 3 3] 3 'bn_conv1' Batch Normalization Batch normalization with 64 channels 4 'activation_1_relu' ReLU ReLU 5 'max_pooling2d_1' Max Pooling 3×3 max pooling with stride [2 2] and padding [1 1 1 1] 6 'res2a_branch2a' Convolution 64 1×1×64 convolutions with stride [1 1] and padding [0 0 0 0] 7 'bn2a_branch2a' Batch Normalization Batch normalization with 64 channels 8 'activation_2_relu' ReLU ReLU 9 'res2a_branch2b' Convolution 64 3×3×64 convolutions with stride [1 1] and padding 'same' 10 'bn2a_branch2b' Batch Normalization Batch normalization with 64 channels 11 'activation_3_relu' ReLU ReLU 12 'res2a_branch2c' Convolution 256 1×1×64 convolutions with stride [1 1] and padding [0 0 0 0] 13 'res2a_branch1' Convolution 256 1×1×64 convolutions with stride [1 1] and padding [0 0 0 0] 14 'bn2a_branch2c' Batch Normalization Batch normalization with 256 channels 15 'bn2a_branch1' Batch Normalization Batch normalization with 256 channels 16 'add_1' Addition Element-wise addition of 2 inputs 17 'activation_4_relu' ReLU ReLU 18 'res2b_branch2a' Convolution 64 1×1×256 convolutions with stride [1 1] and padding [0 0 0 0] 19 'bn2b_branch2a' Batch Normalization Batch normalization with 64 channels 20 'activation_5_relu' ReLU ReLU 21 'res2b_branch2b' Convolution 64 3×3×64 convolutions with stride [1 1] and padding 'same' 22 'bn2b_branch2b' Batch Normalization Batch normalization with 64 channels 23 'activation_6_relu' ReLU ReLU 24 'res2b_branch2c' Convolution 256 1×1×64 convolutions with stride [1 1] and padding [0 0 0 0] 25 'bn2b_branch2c' Batch Normalization Batch normalization with 256 channels 26 'add_2' Addition Element-wise addition of 2 inputs 27 'activation_7_relu' ReLU ReLU 28 'res2c_branch2a' Convolution 64 1×1×256 convolutions with stride [1 1] and padding [0 0 0 0] 29 'bn2c_branch2a' Batch Normalization Batch normalization with 64 channels 30 'activation_8_relu' ReLU ReLU 31 'res2c_branch2b' Convolution 64 3×3×64 convolutions with stride [1 1] and padding 'same' 32 'bn2c_branch2b' Batch Normalization Batch normalization with 64 channels 33 'activation_9_relu' ReLU ReLU 34 'res2c_branch2c' Convolution 256 1×1×64 convolutions with stride [1 1] and padding [0 0 0 0] 35 'bn2c_branch2c' Batch Normalization Batch normalization with 256 channels 36 'add_3' Addition Element-wise addition of 2 inputs 37 'activation_10_relu' ReLU ReLU 38 'res3a_branch2a' Convolution 128 1×1×256 convolutions with stride [2 2] and padding [0 0 0 0] 39 'bn3a_branch2a' Batch Normalization Batch normalization with 128 channels 40 'activation_11_relu' ReLU ReLU 41 'res3a_branch2b' Convolution 128 3×3×128 convolutions with stride [1 1] and padding 'same' 42 'bn3a_branch2b' Batch Normalization Batch normalization with 128 channels 43 'activation_12_relu' ReLU ReLU 44 'res3a_branch2c' Convolution 512 1×1×128 convolutions with stride [1 1] and padding [0 0 0 0] 45 'res3a_branch1' Convolution 512 1×1×256 convolutions with stride [2 2] and padding [0 0 0 0] 46 'bn3a_branch2c' Batch Normalization Batch normalization with 512 channels 47 'bn3a_branch1' Batch Normalization Batch normalization with 512 channels 48 'add_4' Addition Element-wise addition of 2 inputs 49 'activation_13_relu' ReLU ReLU 50 'res3b_branch2a' Convolution 128 1×1×512 convolutions with stride [1 1] and padding [0 0 0 0] 51 'bn3b_branch2a' Batch Normalization Batch normalization with 128 channels 52 'activation_14_relu' ReLU ReLU 53 'res3b_branch2b' Convolution 128 3×3×128 convolutions with stride [1 1] and padding 'same' 54 'bn3b_branch2b' Batch Normalization Batch normalization with 128 channels 55 'activation_15_relu' ReLU ReLU 56 'res3b_branch2c' Convolution 512 1×1×128 convolutions with stride [1 1] and padding [0 0 0 0] 57 'bn3b_branch2c' Batch Normalization Batch normalization with 512 channels 58 'add_5' Addition Element-wise addition of 2 inputs 59 'activation_16_relu' ReLU ReLU 60 'res3c_branch2a' Convolution 128 1×1×512 convolutions with stride [1 1] and padding [0 0 0 0] 61 'bn3c_branch2a' Batch Normalization Batch normalization with 128 channels 62 'activation_17_relu' ReLU ReLU 63 'res3c_branch2b' Convolution 128 3×3×128 convolutions with stride [1 1] and padding 'same' 64 'bn3c_branch2b' Batch Normalization Batch normalization with 128 channels 65 'activation_18_relu' ReLU ReLU 66 'res3c_branch2c' Convolution 512 1×1×128 convolutions with stride [1 1] and padding [0 0 0 0] 67 'bn3c_branch2c' Batch Normalization Batch normalization with 512 channels 68 'add_6' Addition Element-wise addition of 2 inputs 69 'activation_19_relu' ReLU ReLU 70 'res3d_branch2a' Convolution 128 1×1×512 convolutions with stride [1 1] and padding [0 0 0 0] 71 'bn3d_branch2a' Batch Normalization Batch normalization with 128 channels 72 'activation_20_relu' ReLU ReLU 73 'res3d_branch2b' Convolution 128 3×3×128 convolutions with stride [1 1] and padding 'same' 74 'bn3d_branch2b' Batch Normalization Batch normalization with 128 channels 75 'activation_21_relu' ReLU ReLU 76 'res3d_branch2c' Convolution 512 1×1×128 convolutions with stride [1 1] and padding [0 0 0 0] 77 'bn3d_branch2c' Batch Normalization Batch normalization with 512 channels 78 'add_7' Addition Element-wise addition of 2 inputs 79 'activation_22_relu' ReLU ReLU 80 'res4a_branch2a' Convolution 256 1×1×512 convolutions with stride [2 2] and padding [0 0 0 0] 81 'bn4a_branch2a' Batch Normalization Batch normalization with 256 channels 82 'activation_23_relu' ReLU ReLU 83 'res4a_branch2b' Convolution 256 3×3×256 convolutions with stride [1 1] and padding 'same' 84 'bn4a_branch2b' Batch Normalization Batch normalization with 256 channels 85 'activation_24_relu' ReLU ReLU 86 'res4a_branch2c' Convolution 1024 1×1×256 convolutions with stride [1 1] and padding [0 0 0 0] 87 'res4a_branch1' Convolution 1024 1×1×512 convolutions with stride [2 2] and padding [0 0 0 0] 88 'bn4a_branch2c' Batch Normalization Batch normalization with 1024 channels 89 'bn4a_branch1' Batch Normalization Batch normalization with 1024 channels 90 'add_8' Addition Element-wise addition of 2 inputs 91 'activation_25_relu' ReLU ReLU 92 'res4b_branch2a' Convolution 256 1×1×1024 convolutions with stride [1 1] and padding [0 0 0 0] 93 'bn4b_branch2a' Batch Normalization Batch normalization with 256 channels 94 'activation_26_relu' ReLU ReLU 95 'res4b_branch2b' Convolution 256 3×3×256 convolutions with stride [1 1] and padding 'same' 96 'bn4b_branch2b' Batch Normalization Batch normalization with 256 channels 97 'activation_27_relu' ReLU ReLU 98 'res4b_branch2c' Convolution 1024 1×1×256 convolutions with stride [1 1] and padding [0 0 0 0] 99 'bn4b_branch2c' Batch Normalization Batch normalization with 1024 channels 100 'add_9' Addition Element-wise addition of 2 inputs 101 'activation_28_relu' ReLU ReLU 102 'res4c_branch2a' Convolution 256 1×1×1024 convolutions with stride [1 1] and padding [0 0 0 0] 103 'bn4c_branch2a' Batch Normalization Batch normalization with 256 channels 104 'activation_29_relu' ReLU ReLU 105 'res4c_branch2b' Convolution 256 3×3×256 convolutions with stride [1 1] and padding 'same' 106 'bn4c_branch2b' Batch Normalization Batch normalization with 256 channels 107 'activation_30_relu' ReLU ReLU 108 'res4c_branch2c' Convolution 1024 1×1×256 convolutions with stride [1 1] and padding [0 0 0 0] 109 'bn4c_branch2c' Batch Normalization Batch normalization with 1024 channels 110 'add_10' Addition Element-wise addition of 2 inputs 111 'activation_31_relu' ReLU ReLU 112 'res4d_branch2a' Convolution 256 1×1×1024 convolutions with stride [1 1] and padding [0 0 0 0] 113 'bn4d_branch2a' Batch Normalization Batch normalization with 256 channels 114 'activation_32_relu' ReLU ReLU 115 'res4d_branch2b' Convolution 256 3×3×256 convolutions with stride [1 1] and padding 'same' 116 'bn4d_branch2b' Batch Normalization Batch normalization with 256 channels 117 'activation_33_relu' ReLU ReLU 118 'res4d_branch2c' Convolution 1024 1×1×256 convolutions with stride [1 1] and padding [0 0 0 0] 119 'bn4d_branch2c' Batch Normalization Batch normalization with 1024 channels 120 'add_11' Addition Element-wise addition of 2 inputs 121 'activation_34_relu' ReLU ReLU 122 'res4e_branch2a' Convolution 256 1×1×1024 convolutions with stride [1 1] and padding [0 0 0 0] 123 'bn4e_branch2a' Batch Normalization Batch normalization with 256 channels 124 'activation_35_relu' ReLU ReLU 125 'res4e_branch2b' Convolution 256 3×3×256 convolutions with stride [1 1] and padding 'same' 126 'bn4e_branch2b' Batch Normalization Batch normalization with 256 channels 127 'activation_36_relu' ReLU ReLU 128 'res4e_branch2c' Convolution 1024 1×1×256 convolutions with stride [1 1] and padding [0 0 0 0] 129 'bn4e_branch2c' Batch Normalization Batch normalization with 1024 channels 130 'add_12' Addition Element-wise addition of 2 inputs 131 'activation_37_relu' ReLU ReLU 132 'res4f_branch2a' Convolution 256 1×1×1024 convolutions with stride [1 1] and padding [0 0 0 0] 133 'bn4f_branch2a' Batch Normalization Batch normalization with 256 channels 134 'activation_38_relu' ReLU ReLU 135 'res4f_branch2b' Convolution 256 3×3×256 convolutions with stride [1 1] and padding 'same' 136 'bn4f_branch2b' Batch Normalization Batch normalization with 256 channels 137 'activation_39_relu' ReLU ReLU 138 'res4f_branch2c' Convolution 1024 1×1×256 convolutions with stride [1 1] and padding [0 0 0 0] 139 'bn4f_branch2c' Batch Normalization Batch normalization with 1024 channels 140 'add_13' Addition Element-wise addition of 2 inputs 141 'activation_40_relu' ReLU ReLU 142 'res5a_branch2a' Convolution 512 1×1×1024 convolutions with stride [2 2] and padding [0 0 0 0] 143 'bn5a_branch2a' Batch Normalization Batch normalization with 512 channels 144 'activation_41_relu' ReLU ReLU 145 'res5a_branch2b' Convolution 512 3×3×512 convolutions with stride [1 1] and padding 'same' 146 'bn5a_branch2b' Batch Normalization Batch normalization with 512 channels 147 'activation_42_relu' ReLU ReLU 148 'res5a_branch2c' Convolution 2048 1×1×512 convolutions with stride [1 1] and padding [0 0 0 0] 149 'res5a_branch1' Convolution 2048 1×1×1024 convolutions with stride [2 2] and padding [0 0 0 0] 150 'bn5a_branch2c' Batch Normalization Batch normalization with 2048 channels 151 'bn5a_branch1' Batch Normalization Batch normalization with 2048 channels 152 'add_14' Addition Element-wise addition of 2 inputs 153 'activation_43_relu' ReLU ReLU 154 'res5b_branch2a' Convolution 512 1×1×2048 convolutions with stride [1 1] and padding [0 0 0 0] 155 'bn5b_branch2a' Batch Normalization Batch normalization with 512 channels 156 'activation_44_relu' ReLU ReLU 157 'res5b_branch2b' Convolution 512 3×3×512 convolutions with stride [1 1] and padding 'same' 158 'bn5b_branch2b' Batch Normalization Batch normalization with 512 channels 159 'activation_45_relu' ReLU ReLU 160 'res5b_branch2c' Convolution 2048 1×1×512 convolutions with stride [1 1] and padding [0 0 0 0] 161 'bn5b_branch2c' Batch Normalization Batch normalization with 2048 channels 162 'add_15' Addition Element-wise addition of 2 inputs 163 'activation_46_relu' ReLU ReLU 164 'res5c_branch2a' Convolution 512 1×1×2048 convolutions with stride [1 1] and padding [0 0 0 0] 165 'bn5c_branch2a' Batch Normalization Batch normalization with 512 channels 166 'activation_47_relu' ReLU ReLU 167 'res5c_branch2b' Convolution 512 3×3×512 convolutions with stride [1 1] and padding 'same' 168 'bn5c_branch2b' Batch Normalization Batch normalization with 512 channels 169 'activation_48_relu' ReLU ReLU 170 'res5c_branch2c' Convolution 2048 1×1×512 convolutions with stride [1 1] and padding [0 0 0 0] 171 'bn5c_branch2c' Batch Normalization Batch normalization with 2048 channels 172 'add_16' Addition Element-wise addition of 2 inputs 173 'activation_49_relu' ReLU ReLU 174 'avg_pool' 2-D Global Average Pooling 2-D global average pooling 175 'fc1000' Fully Connected 1000 fully connected layer 176 'fc1000_softmax' Softmax softmax 177 'ClassificationLayer_fc1000' Classification Output crossentropyex with 'tench' and 999 other classes