kerasモデルの畳み込みレイヤーの可視化

Question

Keras（私は初心者です）でモデルを作成し、何とかそれをうまく練習することができました。 300x300の画像が2つのグループに分類されます。

# size of image in pixel 
img_rows, img_cols = 300, 300 
# number of classes (here digits 1 to 10) 
nb_classes = 2 
# number of convolutional filters to use 
nb_filters = 16 
# size of pooling area for max pooling 
nb_pool = 20 
# convolution kernel size 
nb_conv = 20 

X = np.vstack([X_train, X_test]).reshape(-1, 1, img_rows, img_cols) 
y = np_utils.to_categorical(np.concatenate([y_train, y_test]), nb_classes) 

# build model 
model = Sequential() 
model.add(Convolution2D(nb_filters, nb_conv, nb_conv, border_mode='valid', input_shape=(1, img_rows, img_cols))) 
model.add(Activation('relu')) 
model.add(Convolution2D(nb_filters, nb_conv, nb_conv)) 
model.add(Activation('relu')) 
model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool))) 
model.add(Dropout(0.25)) 
model.add(Flatten()) 
model.add(Dense(64)) 
model.add(Activation('relu')) 
model.add(Dropout(0.5)) 
model.add(Dense(nb_classes)) 
model.add(Activation('softmax')) 

# run model 
model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy']) 

今、私は2番目の畳み込み層と、可能な場合も、最初の緻密層を視覚化したいと思います。 「インスピレーション」はkeras blogから取られました。 model.summary()を使用すると、レイヤの名前がわかりました。それから私は、次のフランケンシュタインのコードを作成しました：

from __future__ import print_function 
from scipy.misc import imsave 
import numpy as np 
import time 
#from keras.applications import vgg16 
import keras 
from keras import backend as K 

# dimensions of the generated pictures for each filter. 
img_width = 300 
img_height = 300 

# the name of the layer we want to visualize 
# (see model definition at keras/applications/vgg16.py) 
layer_name = 'convolution2d_2' 
#layer_name = 'dense_1' 

# util function to convert a tensor into a valid image 
def deprocess_image(x): 
    # normalize tensor: center on 0., ensure std is 0.1 
    x -= x.mean() 
    x /= (x.std() + 1e-5) 
    x *= 0.1 

    # clip to [0, 1] 
    x += 0.5 
    x = np.clip(x, 0, 1) 

    # convert to RGB array 
    x *= 255 
    if K.image_dim_ordering() == 'th': 
     x = x.transpose((1, 2, 0)) 
    x = np.clip(x, 0, 255).astype('uint8') 
    return x 

# load model 
loc_json = 'my_model_short_architecture.json' 
loc_h5 = 'my_model_short_weights.h5' 

with open(loc_json, 'r') as json_file: 
    loaded_model_json = json_file.read() 

model = keras.models.model_from_json(loaded_model_json) 

# load weights into new model 
model.load_weights(loc_h5) 
print('Model loaded.') 

model.summary() 

# this is the placeholder for the input images 
input_img = model.input 

# get the symbolic outputs of each "key" layer (we gave them unique names). 
layer_dict = dict([(layer.name, layer) for layer in model.layers[1:]]) 


def normalize(x): 
    # utility function to normalize a tensor by its L2 norm 
    return x/(K.sqrt(K.mean(K.square(x))) + 1e-5) 


kept_filters = [] 
for filter_index in range(0, 200): 
    # we only scan through the first 200 filters, 
    # but there are actually 512 of them 
    print('Processing filter %d' % filter_index) 
    start_time = time.time() 

    # we build a loss function that maximizes the activation 
    # of the nth filter of the layer considered 
    layer_output = layer_dict[layer_name].output 
    if K.image_dim_ordering() == 'th': 
     loss = K.mean(layer_output[:, filter_index, :, :]) 
    else: 
     loss = K.mean(layer_output[:, :, :, filter_index]) 


    # we compute the gradient of the input picture wrt this loss 
    grads = K.gradients(loss, input_img)[0] 

    # normalization trick: we normalize the gradient 
    grads = normalize(grads) 

    # this function returns the loss and grads given the input picture 
    iterate = K.function([input_img], [loss, grads]) 

    # step size for gradient ascent 
    step = 1. 

    # we start from a gray image with some random noise 
    if K.image_dim_ordering() == 'th': 
     input_img_data = np.random.random((1, 3, img_width, img_height)) 
    else: 
     input_img_data = np.random.random((1, img_width, img_height, 3)) 
    input_img_data = (input_img_data - 0.5) * 20 + 128 

    # we run gradient ascent for 20 steps 
    for i in range(20): 
     loss_value, grads_value = iterate([input_img_data]) 
     input_img_data += grads_value * step 

     print('Current loss value:', loss_value) 
     if loss_value <= 0.: 
      # some filters get stuck to 0, we can skip them 
      break 

    # decode the resulting input image 
    if loss_value > 0: 
     img = deprocess_image(input_img_data[0]) 
     kept_filters.append((img, loss_value)) 
    end_time = time.time() 
    print('Filter %d processed in %ds' % (filter_index, end_time - start_time)) 

# we will stich the best 64 filters on a 8 x 8 grid. 
n = 8 

# the filters that have the highest loss are assumed to be better-looking. 
# we will only keep the top 64 filters. 
kept_filters.sort(key=lambda x: x[1], reverse=True) 
kept_filters = kept_filters[:n * n] 

# build a black picture with enough space for 
# our 8 x 8 filters of size 128 x 128, with a 5px margin in between 
margin = 5 
width = n * img_width + (n - 1) * margin 
height = n * img_height + (n - 1) * margin 
stitched_filters = np.zeros((width, height, 3)) 

# fill the picture with our saved filters 
for i in range(n): 
    for j in range(n): 
     img, loss = kept_filters[i * n + j] 
     stitched_filters[(img_width + margin) * i: (img_width + margin) * i + img_width, 
         (img_height + margin) * j: (img_height + margin) * j + img_height, :] = img 

# save the result to disk 
imsave('stitched_filters_%dx%d.png' % (n, n), stitched_filters) 

それを実行した後、私は得る：

ValueError        Traceback (most recent call last) 
/home/user/conv_filter_visualization.py in <module>() 
    97  # we run gradient ascent for 20 steps 
/home/user/.local/lib/python3.4/site-packages/theano/compile/function_module.py in __call__(self, *args, **kwargs) 
    857   t0_fn = time.time() 
    858   try: 
--> 859    outputs = self.fn() 
    860   except Exception: 
    861    if hasattr(self.fn, 'position_of_error'): 

ValueError: CorrMM images and kernel must have the same stack size 

Apply node that caused the error: CorrMM{valid, (1, 1)}(convolution2d_input_1, Subtensor{::, ::, ::int64, ::int64}.0) 
Toposort index: 8 
Inputs types: [TensorType(float32, 4D), TensorType(float32, 4D)] 
Inputs shapes: [(1, 3, 300, 300), (16, 1, 20, 20)] 
Inputs strides: [(1080000, 360000, 1200, 4), (1600, 1600, -80, -4)] 
Inputs values: ['not shown', 'not shown'] 
Outputs clients: [[Elemwise{add,no_inplace}(CorrMM{valid, (1, 1)}.0, Reshape{4}.0), Elemwise{Composite{(i0 * (Abs(i1) + i2 + i3))}}[(0, 1)](TensorConstant{(1, 1, 1, 1) of 0.5}, Elemwise{add,no_inplace}.0, CorrMM{valid, (1, 1)}.0, Reshape{4}.0)]] 

Backtrace when the node is created(use Theano flag traceback.limit=N to make it longer): 
    File "/home/user/.local/lib/python3.4/site-packages/keras/models.py", line 787, in from_config 
    model.add(layer) 
    File "/home/user/.local/lib/python3.4/site-packages/keras/models.py", line 114, in add 
    layer.create_input_layer(batch_input_shape, input_dtype) 
    File "/home/user/.local/lib/python3.4/site-packages/keras/engine/topology.py", line 341, in create_input_layer 
    self(x) 
    File "/home/user/.local/lib/python3.4/site-packages/keras/engine/topology.py", line 485, in __call__ 
    self.add_inbound_node(inbound_layers, node_indices, tensor_indices) 
    File "/home/user/.local/lib/python3.4/site-packages/keras/engine/topology.py", line 543, in add_inbound_node 
    Node.create_node(self, inbound_layers, node_indices, tensor_indices) 
    File "/home/user/.local/lib/python3.4/site-packages/keras/engine/topology.py", line 148, in create_node 
    output_tensors = to_list(outbound_layer.call(input_tensors[0], mask=input_masks[0])) 
    File "/home/user/.local/lib/python3.4/site-packages/keras/layers/convolutional.py", line 356, in call 
    filter_shape=self.W_shape) 
    File "/home/user/.local/lib/python3.4/site-packages/keras/backend/theano_backend.py", line 862, in conv2d 
    filter_shape=filter_shape) 

私は、私はいくつかの悪い寸法を有する午前推測するが、それでもどこから始めれば分かりません。どんな助けもありがとう。ありがとう。

Answer 1

Kerasを使用すると、レイヤーの重みと出力を簡単に取得できます。 https://keras.io/layers/about-keras-layers/またはhttps://keras.io/getting-started/functional-api-guide/#the-concept-of-layer-nodeをご覧ください。

基本的には、各レイヤのプロパティweightsとoutputで取得できます。

Answer 2

ネットワークでは、最初の畳み込みレイヤには16のフィルタしかなく、次の畳み込みレイヤには16のフィルタしかないので、32の畳み込みフィルタがあります。しかし、あなたは200 forループを実行しています。それを16または32に変更してみてください。私はこのコードをTFバックエンドで実行していて、私の小さなCNNのために働いています。 また、画像ステッチコードを変更：幸運の

for i in range(n): 
    for j in range(n): 
     if(i * n + j)<=len(kept_filters)-1: 

ベスト...

Answer 3

は、このプロジェクトを見てください：

https://github.com/philipperemy/keras-visualize-activations

あなたは、すべてのアクティベーションマップを抽出することができます層。これはすべてのKerasモデルで機能します。