Check failed: ShapeEquals(proto) shape mismatch (reshape not set)

[Rafael Ruiz Muñoz]

Hello.

I have this error and I have tried to take a look in Internet but I got nothing clear.

I trained my net successfully with around 82% of accuracy.

Now I'm trying to try it with an image through this code:

python python/classify.py --model_def examples/imagenet/imagenet_deploy.prototxt --pretrained_model caffe_mycaffe_train_iter_10000.caffemodel --images_dim 64,64 data/mycaffe/testingset/cat1/113.png foo --mean_file data/mycaffe/mycaffe_train_mean.binaryproto

yes, my images are 64x64, 

these are the last lines I'm getting:

I0610 15:33:44.868100 28657 net.cpp:194] conv3 does not need backward computation.
I0610 15:33:44.868110 28657 net.cpp:194] norm2 does not need backward computation.
I0610 15:33:44.868120 28657 net.cpp:194] pool2 does not need backward computation.
I0610 15:33:44.868130 28657 net.cpp:194] relu2 does not need backward computation.
I0610 15:33:44.868142 28657 net.cpp:194] conv2 does not need backward computation.
I0610 15:33:44.868152 28657 net.cpp:194] norm1 does not need backward computation.
I0610 15:33:44.868162 28657 net.cpp:194] pool1 does not need backward computation.
I0610 15:33:44.868173 28657 net.cpp:194] relu1 does not need backward computation.
I0610 15:33:44.868182 28657 net.cpp:194] conv1 does not need backward computation.
I0610 15:33:44.868192 28657 net.cpp:235] This network produces output fc8_pascal
I0610 15:33:44.868214 28657 net.cpp:482] Collecting Learning Rate and Weight Decay.
I0610 15:33:44.868238 28657 net.cpp:247] Network initialization done.
I0610 15:33:44.868249 28657 net.cpp:248] Memory required for data: 3136120
F0610 15:33:45.025965 28657 blob.cpp:458] Check failed: ShapeEquals(proto) shape mismatch (reshape not set)
*** Check failure stack trace: ***
Aborted (core dumped)

I've tried to not setting the --mean_file and more things, but my shots are over. 

This is my imagenet_deploy.prototxt which I've modified in some parameters to debug, but didn't work anything.

name: "MyCaffe"
input: "data"
input_dim: 10
input_dim: 3
input_dim: 64
input_dim: 64
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  convolution_param {
    num_output: 64
    kernel_size: 11
    stride: 4
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "norm1"
  top: "conv2"
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    group: 2
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    group: 2
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc6"
  inner_product_param {
    num_output: 4096
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc6"
  top: "fc6"
}
layer {
  name: "drop6"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  inner_product_param {
    num_output: 4096
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "fc7"
  top: "fc7"
}
layer {
  name: "drop7"
  type: "Dropout"
  bottom: "fc7"
  top: "fc7"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc8_pascal"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8_pascal"
  inner_product_param {
    num_output: 3
  }
}

Does anyone could give me a clue?

Thank you very much.