визуализация сверточного слоя в модели keras

Я создал модель в Keras (я новичок) и каким-то образом смог хорошо ее обучить. Он принимает 300×300 изображений и пытается классифицировать их в двух группах.

# 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']) 

Теперь я хотел бы визуализировать второй сверточный слой и, если возможно, также первый плотный слой. «Вдохновение» было взято из блога keras . Используя model.summary() я узнал название слоев. Затем я создал следующий код frankenstein:

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 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) 

После его выполнения я получаю:

 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) 

Наверное, у меня плохие измерения, но я даже не знаю, с чего начать. Любая помощь будет оценена по достоинству. Благодарю.

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