如何在keras

Question

我的CNN模型包含卷积层和密集层。我可以借助下面的代码可视化图像和滤波层的滤波器，但是在密集层之后无法看到输出图像（只有图像，因为没有滤波器）。当我尝试使用下面的代码时，我收到错误：

 File "<ipython-input-25-e8e4d4494672>", line 35, in <module>
    num_of_featuremaps=feature_maps.shape[2]

IndexError: tuple index out of range
#and after that some blank space 

代码如下：

def get_featuremaps(model, layer_idx, X_batch):
    get_activations = K.function([model.layers[0].input, K.learning_phase()],[model.layers[layer_idx].output,])
    activations = get_activations([X_batch,0])
    return activations

layer_num=11
filter_num=0
test_image=x[0]
test_image_show=test_image[:,:,0]
plt.axis('off')
test_image= np.expand_dims(test_image, axis=0)
print (test_image.shape)
activations = get_featuremaps(model, int(layer_num),test_image)

print (np.shape(activations))
feature_maps = activations[0][0]      
print (np.shape(feature_maps))

if K.image_dim_ordering()=='th':
    feature_maps=np.rollaxis((np.rollaxis(feature_maps,2,0)),2,0)

print (feature_maps.shape)

fig=plt.figure(figsize=(16,16))
#plt.imshow(feature_maps[:,:,filter_num],cmap='gray')

#plt.savefig("featuremaps-layer-{}".format(layer_num) + "-filternum-{}".format(filter_num)+'.jpg')

num_of_featuremaps=feature_maps.shape[2]
fig=plt.figure(figsize=(16,16)) 
plt.title("featuremaps-layer-{}".format(layer_num))
subplot_num=int(np.ceil(np.sqrt(num_of_featuremaps)))
for i in range(int(num_of_featuremaps)):
    ax = fig.add_subplot(subplot_num, subplot_num, i+1)
    ax.imshow(feature_maps[:,:,i],cmap='gray')
    plt.xticks([])
    plt.yticks([])
    plt.tight_layout()

plt.show()

from mpl_toolkits.axes_grid1 import make_axes_locatable
def nice_imshow(ax, data, vmin=None, vmax=None, cmap=None):
    """Wrapper around pl.imshow"""
    if cmap is None:
        cmap = cm.jet
    if vmin is None:
        vmin = data.min()
    if vmax is None:
        vmax = data.max()
    divider = make_axes_locatable(ax)
    cax = divider.append_axes("right", size="5%", pad=0.05)
    im = ax.imshow(data, vmin=vmin, vmax=vmax, interpolation='nearest', cmap=cmap)
    pl.colorbar(im, cax=cax)

模型如下所示：

Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_37 (Conv2D)           (None, 49, 49, 32)        160       
_________________________________________________________________
conv2d_38 (Conv2D)           (None, 48, 48, 32)        4128      
_________________________________________________________________
max_pooling2d_19 (MaxPooling (None, 24, 24, 32)        0         
_________________________________________________________________
dropout_28 (Dropout)         (None, 24, 24, 32)        0         
_________________________________________________________________
conv2d_39 (Conv2D)           (None, 23, 23, 64)        8256      
_________________________________________________________________
conv2d_40 (Conv2D)           (None, 22, 22, 64)        16448     
_________________________________________________________________
max_pooling2d_20 (MaxPooling (None, 11, 11, 64)        0         
_________________________________________________________________
dropout_29 (Dropout)         (None, 11, 11, 64)        0         
_________________________________________________________________
flatten_10 (Flatten)         (None, 7744)              0         
_________________________________________________________________
dense_19 (Dense)             (None, 256)               1982720   
_________________________________________________________________
dropout_30 (Dropout)         (None, 256)               0         
_________________________________________________________________
dense_20 (Dense)             (None, 2)                 514       
=================================================================
Total params: 2,012,226
Trainable params: 2,012,226
Non-trainable params: 0
_____________________________________

其中pl是pylab而plt是matplotlib。