Question

在学习CNN时，我发现这个博客就像打击

他使用C语言做cnn，这是参考Matlab DeepLearnToolbox cnn。代码就像打击

//---forward Propagation,InputData is image data
void cnnff(CNN* cnn,float** inputData)
{
    int outSizeW=cnn->S2->inputWidth;
    int outSizeH=cnn->S2->inputHeight;
    int i,j,r,c;

    //---the first,convolution C1
    nSize mapSize={cnn->C1->mapSize,cnn->C1->mapSize};
    nSize inSize={cnn->C1->inputWidth,cnn->C1->inputHeight};
    nSize outSize={cnn->S2->inputWidth,cnn->S2->inputHeight};
    for(i=0;i<(cnn->C1->outChannels);i++){
        for(j=0;j<(cnn->C1->inChannels);j++){
            float** mapout=cov(cnn->C1->mapData[j][i],mapSize,inputData,inSize,valid);
            addmat(cnn->C1->v[i],cnn->C1->v[i],outSize,mapout,outSize);
            for(r=0;r<outSize.r;r++)
                free(mapout[r]);
            free(mapout);
        }
        for(r=0;r<outSize.r;r++)
            for(c=0;c<outSize.c;c++)
                cnn->C1->y[i][r][c]=activation_Sigma(cnn->C1->v[i][r][c],cnn->C1->basicData[i]);
    }

    //the second，pooling S2
    outSize.c=cnn->C3->inputWidth;
    outSize.r=cnn->C3->inputHeight;
    inSize.c=cnn->S2->inputWidth;
    inSize.r=cnn->S2->inputHeight;
    for(i=0;i<(cnn->S2->outChannels);i++){
        if(cnn->S2->poolType==AvePool)
            avgPooling(cnn->S2->y[i],outSize,cnn->C1->y[i],inSize,cnn->S2->mapSize);
    }
}

这段代码我可以看到输入图像卷积和合并后的值如何改变以及有多少特征映射。那么，我可以在Tensorflow上看到这样吗？

我在tensorflow \ python \ client \ session.py上跟踪Tensorflow代码，代码就像打击

def _run_fn(session, feed_dict, fetch_list, target_list, options,
            run_metadata):
  # Ensure any changes to the graph are reflected in the runtime.
  self._extend_graph()
  with errors.raise_exception_on_not_ok_status() as status:
    if self._created_with_new_api:
      return tf_session.TF_SessionRun_wrapper(
          session, options, feed_dict, fetch_list, target_list,
          run_metadata, status)
    else:
      return tf_session.TF_Run(session, options,
                               feed_dict, fetch_list, target_list,
                               status, run_metadata)

什么时候执行功能＆＃34; tf_session.TF_Run＆＃34;它只返回（丢失，准确度）），但无法看到如何改变的价值。

然后我在C：\ Users \ xxx \ AppData \ Local \ Continuum \ Anaconda3 \ envs \ tensorflow1 \ Lib \ site-packages \ tensorflow \ python \ pywrap_tensorflow_internal.py中跟踪Tensorflow代码，代码就像打击

def TF_Run(session, run_options, feed_dict, output_names, target_nodes, out_status, run_outputs):
    return _pywrap_tensorflow_internal.TF_Run(session, run_options, feed_dict, output_names, target_nodes, out_status, run_outputs)
TF_Run = _pywrap_tensorflow_internal.TF_Run

pywrap_tensorflow_internal.py使用_pywrap_tensorflow_internal.pyd，我认为如何更改的值是在这个.pyd上。那么，这个.pyd源代码在哪里？因为这个.pyd只能通过＆＃34; pip install tensorflow＆＃34;来下载。

Answer 1

希望您正在寻找CNN的内部运作方式。以下代码说明了内部如何执行转换操作。下面的代码等效于该张量流API。

有关此特定操作的更多信息，请参见How to map input image with neurons in first conv layer in CNN?

注意：这只是CNN第一层中的转换操作。

Z1 = tf.nn.conv2d(X,W1, strides = [1,1,1,1], padding = 'SAME')

def conv_forward(A_prev, W, b, hparameters):
"""
Implements the forward propagation for a convolution function

Arguments:
A_prev -- output activations of the previous layer, numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)
W -- Weights, numpy array of shape (f, f, n_C_prev, n_C)
b -- Biases, numpy array of shape (1, 1, 1, n_C)
hparameters -- python dictionary containing "stride" and "pad"

Returns:
Z -- conv output, numpy array of shape (m, n_H, n_W, n_C)
cache -- cache of values needed for the conv_backward() function
"""

# Retrieve dimensions from A_prev's shape (≈1 line)  
(m, n_H_prev, n_W_prev, n_C_prev) = A_prev.shape

# Retrieve dimensions from W's shape (≈1 line)
(f, f, n_C_prev, n_C) = W.shape

# Retrieve information from "hparameters" (≈2 lines)
stride = hparameters['stride']
pad = hparameters['pad']

# Compute the dimensions of the CONV output volume using the formula given above. Hint: use int() to floor. (≈2 lines)
n_H = int(np.floor((n_H_prev-f+2*pad)/stride)) + 1
n_W = int(np.floor((n_W_prev-f+2*pad)/stride)) + 1

# Initialize the output volume Z with zeros. (≈1 line)
Z = np.zeros((m,n_H,n_W,n_C))

# Create A_prev_pad by padding A_prev
A_prev_pad = zero_pad(A_prev,pad)

for i in range(m):                               # loop over the batch of training examples
    a_prev_pad = A_prev_pad[i]                               # Select ith training example's padded activation
    for h in range(n_H):                           # loop over vertical axis of the output volume
        for w in range(n_W):                       # loop over horizontal axis of the output volume
            for c in range(n_C):                   # loop over channels (= #filters) of the output volume

                # Find the corners of the current "slice" (≈4 lines)
                vert_start = h*stride
                vert_end = vert_start+f
                horiz_start = w*stride
                horiz_end = horiz_start+f

                # Use the corners to define the (3D) slice of a_prev_pad (See Hint above the cell). (≈1 line)
                a_slice_prev = a_prev_pad[vert_start:vert_end,horiz_start:horiz_end,:]

                # Convolve the (3D) slice with the correct filter W and bias b, to get back one output neuron. (≈1 line)
                Z[i, h, w, c] = conv_single_step(a_slice_prev,W[:,:,:,c],b[:,:,:,c])                                      

return Z


    A_prev = np.random.randn(1,64,64,3)
    W = np.random.randn(4,4,3,8)
    #Don't worry about bias , tensorflow will take care of this.
    b = np.random.randn(1,1,1,8)
    hparameters = {"pad" : 1,
                   "stride": 1}

    Z = conv_forward(A_prev, W, b, hparameters)

Answer 2

pyd文件类似于Windows动态库。

这可以帮助： https://stackabuse.com/differences-between-pyc-pyd-and-pyo-python-files/

也许您需要了解如何从头开始编译tensorflow才能了解该.pyd的所有麻烦;）

Answer 3

我想你的意思是你想知道tf.nn.conv2d和排序是如何实现的。如果您是TensorFlow的新手，您会注意到有layers（例如tf.layers.conv2d）和nn（例如tf.nn.conv2d）。 layers都是nn的包装器，因此，如果您只是想立即开始实施，请忽略layers。

现在，如果您阅读documentation for tf.nn.conv2d，它会显示：

在生成的文件中定义：tensorflow/python/ops/gen_nn_ops.py。

为便于比较，请看一下documentation for tf.nn.conv2d_transpose，它表示：

在tensorflow/python/ops/nn_ops.py中定义。

现在，如果您单击tensorflow/python/ops/nn_ops.py，实际上会将您带到定义了tf.nn.conv2d_transpose的文件。但是对于您感兴趣的tf.nn.conv2d，此链接不存在。这是因为您可以使用C ++编写该层，并让TensorFlow生成Python部分，因此可以在生成的文件中定义文本。实际的实现分布在这里的三个文件中：

张量流是如何进行CNN计算的？

3 个答案: