La red que confundió a mi mujer con un sombrero¶
In [1]:
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
In [2]:
import tensorflow as tf
from tensorflow import keras
from keras.applications.inception_v3 import InceptionV3, decode_predictions
from keras import backend as K
###################################
# TensorFlow wizardry
config = tf.ConfigProto()
# Don't pre-allocate memory; allocate as-needed
config.gpu_options.allow_growth = True
# Only allow a total of half the GPU memory to be allocated
#config.gpu_options.per_process_gpu_memory_fraction = 0.01
# Create a session with the above options specified.
K.tensorflow_backend.set_session(tf.Session(config=config))
###################################
Using TensorFlow backend.
Inception V3¶
- Ganador de ILSVRC (ImageNet Large Scale Visual Recognition Competition) 2015
- Entrenado con ImageNet (solo 1000 categorias)
- ImageNet, es una base de datos de más de 15 millones de imágenes etiquetadas de alta resolución con alrededor de 22,000 categorÃas
In [3]:
iv3 = InceptionV3()
print(iv3.summary())
WARNING:tensorflow:From /home/damian/anaconda3/envs/dotcsv/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 299, 299, 3) 0
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 149, 149, 32) 864 input_1[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 149, 149, 32) 96 conv2d_1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 149, 149, 32) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 147, 147, 32) 9216 activation_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 147, 147, 32) 96 conv2d_2[0][0]
__________________________________________________________________________________________________
activation_2 (Activation) (None, 147, 147, 32) 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 147, 147, 64) 18432 activation_2[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 147, 147, 64) 192 conv2d_3[0][0]
__________________________________________________________________________________________________
activation_3 (Activation) (None, 147, 147, 64) 0 batch_normalization_3[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 73, 73, 64) 0 activation_3[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 73, 73, 80) 5120 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 73, 73, 80) 240 conv2d_4[0][0]
__________________________________________________________________________________________________
activation_4 (Activation) (None, 73, 73, 80) 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 71, 71, 192) 138240 activation_4[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 71, 71, 192) 576 conv2d_5[0][0]
__________________________________________________________________________________________________
activation_5 (Activation) (None, 71, 71, 192) 0 batch_normalization_5[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 35, 35, 192) 0 activation_5[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 35, 35, 64) 12288 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 35, 35, 64) 192 conv2d_9[0][0]
__________________________________________________________________________________________________
activation_9 (Activation) (None, 35, 35, 64) 0 batch_normalization_9[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 35, 35, 48) 9216 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 35, 35, 96) 55296 activation_9[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 35, 35, 48) 144 conv2d_7[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 35, 35, 96) 288 conv2d_10[0][0]
__________________________________________________________________________________________________
activation_7 (Activation) (None, 35, 35, 48) 0 batch_normalization_7[0][0]
__________________________________________________________________________________________________
activation_10 (Activation) (None, 35, 35, 96) 0 batch_normalization_10[0][0]
__________________________________________________________________________________________________
average_pooling2d_1 (AveragePoo (None, 35, 35, 192) 0 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 35, 35, 64) 12288 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 35, 35, 64) 76800 activation_7[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 35, 35, 96) 82944 activation_10[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 35, 35, 32) 6144 average_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 35, 35, 64) 192 conv2d_6[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 35, 35, 64) 192 conv2d_8[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 35, 35, 96) 288 conv2d_11[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 35, 35, 32) 96 conv2d_12[0][0]
__________________________________________________________________________________________________
activation_6 (Activation) (None, 35, 35, 64) 0 batch_normalization_6[0][0]
__________________________________________________________________________________________________
activation_8 (Activation) (None, 35, 35, 64) 0 batch_normalization_8[0][0]
__________________________________________________________________________________________________
activation_11 (Activation) (None, 35, 35, 96) 0 batch_normalization_11[0][0]
__________________________________________________________________________________________________
activation_12 (Activation) (None, 35, 35, 32) 0 batch_normalization_12[0][0]
__________________________________________________________________________________________________
mixed0 (Concatenate) (None, 35, 35, 256) 0 activation_6[0][0]
activation_8[0][0]
activation_11[0][0]
activation_12[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 35, 35, 64) 16384 mixed0[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 35, 35, 64) 192 conv2d_16[0][0]
__________________________________________________________________________________________________
activation_16 (Activation) (None, 35, 35, 64) 0 batch_normalization_16[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 35, 35, 48) 12288 mixed0[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 35, 35, 96) 55296 activation_16[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 35, 35, 48) 144 conv2d_14[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 35, 35, 96) 288 conv2d_17[0][0]
__________________________________________________________________________________________________
activation_14 (Activation) (None, 35, 35, 48) 0 batch_normalization_14[0][0]
__________________________________________________________________________________________________
activation_17 (Activation) (None, 35, 35, 96) 0 batch_normalization_17[0][0]
__________________________________________________________________________________________________
average_pooling2d_2 (AveragePoo (None, 35, 35, 256) 0 mixed0[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 35, 35, 64) 16384 mixed0[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 35, 35, 64) 76800 activation_14[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 35, 35, 96) 82944 activation_17[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, 35, 35, 64) 16384 average_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 35, 35, 64) 192 conv2d_13[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 35, 35, 64) 192 conv2d_15[0][0]
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 35, 35, 96) 288 conv2d_18[0][0]
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 35, 35, 64) 192 conv2d_19[0][0]
__________________________________________________________________________________________________
activation_13 (Activation) (None, 35, 35, 64) 0 batch_normalization_13[0][0]
__________________________________________________________________________________________________
activation_15 (Activation) (None, 35, 35, 64) 0 batch_normalization_15[0][0]
__________________________________________________________________________________________________
activation_18 (Activation) (None, 35, 35, 96) 0 batch_normalization_18[0][0]
__________________________________________________________________________________________________
activation_19 (Activation) (None, 35, 35, 64) 0 batch_normalization_19[0][0]
__________________________________________________________________________________________________
mixed1 (Concatenate) (None, 35, 35, 288) 0 activation_13[0][0]
activation_15[0][0]
activation_18[0][0]
activation_19[0][0]
__________________________________________________________________________________________________
conv2d_23 (Conv2D) (None, 35, 35, 64) 18432 mixed1[0][0]
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, 35, 35, 64) 192 conv2d_23[0][0]
__________________________________________________________________________________________________
activation_23 (Activation) (None, 35, 35, 64) 0 batch_normalization_23[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D) (None, 35, 35, 48) 13824 mixed1[0][0]
__________________________________________________________________________________________________
conv2d_24 (Conv2D) (None, 35, 35, 96) 55296 activation_23[0][0]
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 35, 35, 48) 144 conv2d_21[0][0]
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, 35, 35, 96) 288 conv2d_24[0][0]
__________________________________________________________________________________________________
activation_21 (Activation) (None, 35, 35, 48) 0 batch_normalization_21[0][0]
__________________________________________________________________________________________________
activation_24 (Activation) (None, 35, 35, 96) 0 batch_normalization_24[0][0]
__________________________________________________________________________________________________
average_pooling2d_3 (AveragePoo (None, 35, 35, 288) 0 mixed1[0][0]
__________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, 35, 35, 64) 18432 mixed1[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D) (None, 35, 35, 64) 76800 activation_21[0][0]
__________________________________________________________________________________________________
conv2d_25 (Conv2D) (None, 35, 35, 96) 82944 activation_24[0][0]
__________________________________________________________________________________________________
conv2d_26 (Conv2D) (None, 35, 35, 64) 18432 average_pooling2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 35, 35, 64) 192 conv2d_20[0][0]
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 35, 35, 64) 192 conv2d_22[0][0]
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, 35, 35, 96) 288 conv2d_25[0][0]
__________________________________________________________________________________________________
batch_normalization_26 (BatchNo (None, 35, 35, 64) 192 conv2d_26[0][0]
__________________________________________________________________________________________________
activation_20 (Activation) (None, 35, 35, 64) 0 batch_normalization_20[0][0]
__________________________________________________________________________________________________
activation_22 (Activation) (None, 35, 35, 64) 0 batch_normalization_22[0][0]
__________________________________________________________________________________________________
activation_25 (Activation) (None, 35, 35, 96) 0 batch_normalization_25[0][0]
__________________________________________________________________________________________________
activation_26 (Activation) (None, 35, 35, 64) 0 batch_normalization_26[0][0]
__________________________________________________________________________________________________
mixed2 (Concatenate) (None, 35, 35, 288) 0 activation_20[0][0]
activation_22[0][0]
activation_25[0][0]
activation_26[0][0]
__________________________________________________________________________________________________
conv2d_28 (Conv2D) (None, 35, 35, 64) 18432 mixed2[0][0]
__________________________________________________________________________________________________
batch_normalization_28 (BatchNo (None, 35, 35, 64) 192 conv2d_28[0][0]
__________________________________________________________________________________________________
activation_28 (Activation) (None, 35, 35, 64) 0 batch_normalization_28[0][0]
__________________________________________________________________________________________________
conv2d_29 (Conv2D) (None, 35, 35, 96) 55296 activation_28[0][0]
__________________________________________________________________________________________________
batch_normalization_29 (BatchNo (None, 35, 35, 96) 288 conv2d_29[0][0]
__________________________________________________________________________________________________
activation_29 (Activation) (None, 35, 35, 96) 0 batch_normalization_29[0][0]
__________________________________________________________________________________________________
conv2d_27 (Conv2D) (None, 17, 17, 384) 995328 mixed2[0][0]
__________________________________________________________________________________________________
conv2d_30 (Conv2D) (None, 17, 17, 96) 82944 activation_29[0][0]
__________________________________________________________________________________________________
batch_normalization_27 (BatchNo (None, 17, 17, 384) 1152 conv2d_27[0][0]
__________________________________________________________________________________________________
batch_normalization_30 (BatchNo (None, 17, 17, 96) 288 conv2d_30[0][0]
__________________________________________________________________________________________________
activation_27 (Activation) (None, 17, 17, 384) 0 batch_normalization_27[0][0]
__________________________________________________________________________________________________
activation_30 (Activation) (None, 17, 17, 96) 0 batch_normalization_30[0][0]
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, 17, 17, 288) 0 mixed2[0][0]
__________________________________________________________________________________________________
mixed3 (Concatenate) (None, 17, 17, 768) 0 activation_27[0][0]
activation_30[0][0]
max_pooling2d_3[0][0]
__________________________________________________________________________________________________
conv2d_35 (Conv2D) (None, 17, 17, 128) 98304 mixed3[0][0]
__________________________________________________________________________________________________
batch_normalization_35 (BatchNo (None, 17, 17, 128) 384 conv2d_35[0][0]
__________________________________________________________________________________________________
activation_35 (Activation) (None, 17, 17, 128) 0 batch_normalization_35[0][0]
__________________________________________________________________________________________________
conv2d_36 (Conv2D) (None, 17, 17, 128) 114688 activation_35[0][0]
__________________________________________________________________________________________________
batch_normalization_36 (BatchNo (None, 17, 17, 128) 384 conv2d_36[0][0]
__________________________________________________________________________________________________
activation_36 (Activation) (None, 17, 17, 128) 0 batch_normalization_36[0][0]
__________________________________________________________________________________________________
conv2d_32 (Conv2D) (None, 17, 17, 128) 98304 mixed3[0][0]
__________________________________________________________________________________________________
conv2d_37 (Conv2D) (None, 17, 17, 128) 114688 activation_36[0][0]
__________________________________________________________________________________________________
batch_normalization_32 (BatchNo (None, 17, 17, 128) 384 conv2d_32[0][0]
__________________________________________________________________________________________________
batch_normalization_37 (BatchNo (None, 17, 17, 128) 384 conv2d_37[0][0]
__________________________________________________________________________________________________
activation_32 (Activation) (None, 17, 17, 128) 0 batch_normalization_32[0][0]
__________________________________________________________________________________________________
activation_37 (Activation) (None, 17, 17, 128) 0 batch_normalization_37[0][0]
__________________________________________________________________________________________________
conv2d_33 (Conv2D) (None, 17, 17, 128) 114688 activation_32[0][0]
__________________________________________________________________________________________________
conv2d_38 (Conv2D) (None, 17, 17, 128) 114688 activation_37[0][0]
__________________________________________________________________________________________________
batch_normalization_33 (BatchNo (None, 17, 17, 128) 384 conv2d_33[0][0]
__________________________________________________________________________________________________
batch_normalization_38 (BatchNo (None, 17, 17, 128) 384 conv2d_38[0][0]
__________________________________________________________________________________________________
activation_33 (Activation) (None, 17, 17, 128) 0 batch_normalization_33[0][0]
__________________________________________________________________________________________________
activation_38 (Activation) (None, 17, 17, 128) 0 batch_normalization_38[0][0]
__________________________________________________________________________________________________
average_pooling2d_4 (AveragePoo (None, 17, 17, 768) 0 mixed3[0][0]
__________________________________________________________________________________________________
conv2d_31 (Conv2D) (None, 17, 17, 192) 147456 mixed3[0][0]
__________________________________________________________________________________________________
conv2d_34 (Conv2D) (None, 17, 17, 192) 172032 activation_33[0][0]
__________________________________________________________________________________________________
conv2d_39 (Conv2D) (None, 17, 17, 192) 172032 activation_38[0][0]
__________________________________________________________________________________________________
conv2d_40 (Conv2D) (None, 17, 17, 192) 147456 average_pooling2d_4[0][0]
__________________________________________________________________________________________________
batch_normalization_31 (BatchNo (None, 17, 17, 192) 576 conv2d_31[0][0]
__________________________________________________________________________________________________
batch_normalization_34 (BatchNo (None, 17, 17, 192) 576 conv2d_34[0][0]
__________________________________________________________________________________________________
batch_normalization_39 (BatchNo (None, 17, 17, 192) 576 conv2d_39[0][0]
__________________________________________________________________________________________________
batch_normalization_40 (BatchNo (None, 17, 17, 192) 576 conv2d_40[0][0]
__________________________________________________________________________________________________
activation_31 (Activation) (None, 17, 17, 192) 0 batch_normalization_31[0][0]
__________________________________________________________________________________________________
activation_34 (Activation) (None, 17, 17, 192) 0 batch_normalization_34[0][0]
__________________________________________________________________________________________________
activation_39 (Activation) (None, 17, 17, 192) 0 batch_normalization_39[0][0]
__________________________________________________________________________________________________
activation_40 (Activation) (None, 17, 17, 192) 0 batch_normalization_40[0][0]
__________________________________________________________________________________________________
mixed4 (Concatenate) (None, 17, 17, 768) 0 activation_31[0][0]
activation_34[0][0]
activation_39[0][0]
activation_40[0][0]
__________________________________________________________________________________________________
conv2d_45 (Conv2D) (None, 17, 17, 160) 122880 mixed4[0][0]
__________________________________________________________________________________________________
batch_normalization_45 (BatchNo (None, 17, 17, 160) 480 conv2d_45[0][0]
__________________________________________________________________________________________________
activation_45 (Activation) (None, 17, 17, 160) 0 batch_normalization_45[0][0]
__________________________________________________________________________________________________
conv2d_46 (Conv2D) (None, 17, 17, 160) 179200 activation_45[0][0]
__________________________________________________________________________________________________
batch_normalization_46 (BatchNo (None, 17, 17, 160) 480 conv2d_46[0][0]
__________________________________________________________________________________________________
activation_46 (Activation) (None, 17, 17, 160) 0 batch_normalization_46[0][0]
__________________________________________________________________________________________________
conv2d_42 (Conv2D) (None, 17, 17, 160) 122880 mixed4[0][0]
__________________________________________________________________________________________________
conv2d_47 (Conv2D) (None, 17, 17, 160) 179200 activation_46[0][0]
__________________________________________________________________________________________________
batch_normalization_42 (BatchNo (None, 17, 17, 160) 480 conv2d_42[0][0]
__________________________________________________________________________________________________
batch_normalization_47 (BatchNo (None, 17, 17, 160) 480 conv2d_47[0][0]
__________________________________________________________________________________________________
activation_42 (Activation) (None, 17, 17, 160) 0 batch_normalization_42[0][0]
__________________________________________________________________________________________________
activation_47 (Activation) (None, 17, 17, 160) 0 batch_normalization_47[0][0]
__________________________________________________________________________________________________
conv2d_43 (Conv2D) (None, 17, 17, 160) 179200 activation_42[0][0]
__________________________________________________________________________________________________
conv2d_48 (Conv2D) (None, 17, 17, 160) 179200 activation_47[0][0]
__________________________________________________________________________________________________
batch_normalization_43 (BatchNo (None, 17, 17, 160) 480 conv2d_43[0][0]
__________________________________________________________________________________________________
batch_normalization_48 (BatchNo (None, 17, 17, 160) 480 conv2d_48[0][0]
__________________________________________________________________________________________________
activation_43 (Activation) (None, 17, 17, 160) 0 batch_normalization_43[0][0]
__________________________________________________________________________________________________
activation_48 (Activation) (None, 17, 17, 160) 0 batch_normalization_48[0][0]
__________________________________________________________________________________________________
average_pooling2d_5 (AveragePoo (None, 17, 17, 768) 0 mixed4[0][0]
__________________________________________________________________________________________________
conv2d_41 (Conv2D) (None, 17, 17, 192) 147456 mixed4[0][0]
__________________________________________________________________________________________________
conv2d_44 (Conv2D) (None, 17, 17, 192) 215040 activation_43[0][0]
__________________________________________________________________________________________________
conv2d_49 (Conv2D) (None, 17, 17, 192) 215040 activation_48[0][0]
__________________________________________________________________________________________________
conv2d_50 (Conv2D) (None, 17, 17, 192) 147456 average_pooling2d_5[0][0]
__________________________________________________________________________________________________
batch_normalization_41 (BatchNo (None, 17, 17, 192) 576 conv2d_41[0][0]
__________________________________________________________________________________________________
batch_normalization_44 (BatchNo (None, 17, 17, 192) 576 conv2d_44[0][0]
__________________________________________________________________________________________________
batch_normalization_49 (BatchNo (None, 17, 17, 192) 576 conv2d_49[0][0]
__________________________________________________________________________________________________
batch_normalization_50 (BatchNo (None, 17, 17, 192) 576 conv2d_50[0][0]
__________________________________________________________________________________________________
activation_41 (Activation) (None, 17, 17, 192) 0 batch_normalization_41[0][0]
__________________________________________________________________________________________________
activation_44 (Activation) (None, 17, 17, 192) 0 batch_normalization_44[0][0]
__________________________________________________________________________________________________
activation_49 (Activation) (None, 17, 17, 192) 0 batch_normalization_49[0][0]
__________________________________________________________________________________________________
activation_50 (Activation) (None, 17, 17, 192) 0 batch_normalization_50[0][0]
__________________________________________________________________________________________________
mixed5 (Concatenate) (None, 17, 17, 768) 0 activation_41[0][0]
activation_44[0][0]
activation_49[0][0]
activation_50[0][0]
__________________________________________________________________________________________________
conv2d_55 (Conv2D) (None, 17, 17, 160) 122880 mixed5[0][0]
__________________________________________________________________________________________________
batch_normalization_55 (BatchNo (None, 17, 17, 160) 480 conv2d_55[0][0]
__________________________________________________________________________________________________
activation_55 (Activation) (None, 17, 17, 160) 0 batch_normalization_55[0][0]
__________________________________________________________________________________________________
conv2d_56 (Conv2D) (None, 17, 17, 160) 179200 activation_55[0][0]
__________________________________________________________________________________________________
batch_normalization_56 (BatchNo (None, 17, 17, 160) 480 conv2d_56[0][0]
__________________________________________________________________________________________________
activation_56 (Activation) (None, 17, 17, 160) 0 batch_normalization_56[0][0]
__________________________________________________________________________________________________
conv2d_52 (Conv2D) (None, 17, 17, 160) 122880 mixed5[0][0]
__________________________________________________________________________________________________
conv2d_57 (Conv2D) (None, 17, 17, 160) 179200 activation_56[0][0]
__________________________________________________________________________________________________
batch_normalization_52 (BatchNo (None, 17, 17, 160) 480 conv2d_52[0][0]
__________________________________________________________________________________________________
batch_normalization_57 (BatchNo (None, 17, 17, 160) 480 conv2d_57[0][0]
__________________________________________________________________________________________________
activation_52 (Activation) (None, 17, 17, 160) 0 batch_normalization_52[0][0]
__________________________________________________________________________________________________
activation_57 (Activation) (None, 17, 17, 160) 0 batch_normalization_57[0][0]
__________________________________________________________________________________________________
conv2d_53 (Conv2D) (None, 17, 17, 160) 179200 activation_52[0][0]
__________________________________________________________________________________________________
conv2d_58 (Conv2D) (None, 17, 17, 160) 179200 activation_57[0][0]
__________________________________________________________________________________________________
batch_normalization_53 (BatchNo (None, 17, 17, 160) 480 conv2d_53[0][0]
__________________________________________________________________________________________________
batch_normalization_58 (BatchNo (None, 17, 17, 160) 480 conv2d_58[0][0]
__________________________________________________________________________________________________
activation_53 (Activation) (None, 17, 17, 160) 0 batch_normalization_53[0][0]
__________________________________________________________________________________________________
activation_58 (Activation) (None, 17, 17, 160) 0 batch_normalization_58[0][0]
__________________________________________________________________________________________________
average_pooling2d_6 (AveragePoo (None, 17, 17, 768) 0 mixed5[0][0]
__________________________________________________________________________________________________
conv2d_51 (Conv2D) (None, 17, 17, 192) 147456 mixed5[0][0]
__________________________________________________________________________________________________
conv2d_54 (Conv2D) (None, 17, 17, 192) 215040 activation_53[0][0]
__________________________________________________________________________________________________
conv2d_59 (Conv2D) (None, 17, 17, 192) 215040 activation_58[0][0]
__________________________________________________________________________________________________
conv2d_60 (Conv2D) (None, 17, 17, 192) 147456 average_pooling2d_6[0][0]
__________________________________________________________________________________________________
batch_normalization_51 (BatchNo (None, 17, 17, 192) 576 conv2d_51[0][0]
__________________________________________________________________________________________________
batch_normalization_54 (BatchNo (None, 17, 17, 192) 576 conv2d_54[0][0]
__________________________________________________________________________________________________
batch_normalization_59 (BatchNo (None, 17, 17, 192) 576 conv2d_59[0][0]
__________________________________________________________________________________________________
batch_normalization_60 (BatchNo (None, 17, 17, 192) 576 conv2d_60[0][0]
__________________________________________________________________________________________________
activation_51 (Activation) (None, 17, 17, 192) 0 batch_normalization_51[0][0]
__________________________________________________________________________________________________
activation_54 (Activation) (None, 17, 17, 192) 0 batch_normalization_54[0][0]
__________________________________________________________________________________________________
activation_59 (Activation) (None, 17, 17, 192) 0 batch_normalization_59[0][0]
__________________________________________________________________________________________________
activation_60 (Activation) (None, 17, 17, 192) 0 batch_normalization_60[0][0]
__________________________________________________________________________________________________
mixed6 (Concatenate) (None, 17, 17, 768) 0 activation_51[0][0]
activation_54[0][0]
activation_59[0][0]
activation_60[0][0]
__________________________________________________________________________________________________
conv2d_65 (Conv2D) (None, 17, 17, 192) 147456 mixed6[0][0]
__________________________________________________________________________________________________
batch_normalization_65 (BatchNo (None, 17, 17, 192) 576 conv2d_65[0][0]
__________________________________________________________________________________________________
activation_65 (Activation) (None, 17, 17, 192) 0 batch_normalization_65[0][0]
__________________________________________________________________________________________________
conv2d_66 (Conv2D) (None, 17, 17, 192) 258048 activation_65[0][0]
__________________________________________________________________________________________________
batch_normalization_66 (BatchNo (None, 17, 17, 192) 576 conv2d_66[0][0]
__________________________________________________________________________________________________
activation_66 (Activation) (None, 17, 17, 192) 0 batch_normalization_66[0][0]
__________________________________________________________________________________________________
conv2d_62 (Conv2D) (None, 17, 17, 192) 147456 mixed6[0][0]
__________________________________________________________________________________________________
conv2d_67 (Conv2D) (None, 17, 17, 192) 258048 activation_66[0][0]
__________________________________________________________________________________________________
batch_normalization_62 (BatchNo (None, 17, 17, 192) 576 conv2d_62[0][0]
__________________________________________________________________________________________________
batch_normalization_67 (BatchNo (None, 17, 17, 192) 576 conv2d_67[0][0]
__________________________________________________________________________________________________
activation_62 (Activation) (None, 17, 17, 192) 0 batch_normalization_62[0][0]
__________________________________________________________________________________________________
activation_67 (Activation) (None, 17, 17, 192) 0 batch_normalization_67[0][0]
__________________________________________________________________________________________________
conv2d_63 (Conv2D) (None, 17, 17, 192) 258048 activation_62[0][0]
__________________________________________________________________________________________________
conv2d_68 (Conv2D) (None, 17, 17, 192) 258048 activation_67[0][0]
__________________________________________________________________________________________________
batch_normalization_63 (BatchNo (None, 17, 17, 192) 576 conv2d_63[0][0]
__________________________________________________________________________________________________
batch_normalization_68 (BatchNo (None, 17, 17, 192) 576 conv2d_68[0][0]
__________________________________________________________________________________________________
activation_63 (Activation) (None, 17, 17, 192) 0 batch_normalization_63[0][0]
__________________________________________________________________________________________________
activation_68 (Activation) (None, 17, 17, 192) 0 batch_normalization_68[0][0]
__________________________________________________________________________________________________
average_pooling2d_7 (AveragePoo (None, 17, 17, 768) 0 mixed6[0][0]
__________________________________________________________________________________________________
conv2d_61 (Conv2D) (None, 17, 17, 192) 147456 mixed6[0][0]
__________________________________________________________________________________________________
conv2d_64 (Conv2D) (None, 17, 17, 192) 258048 activation_63[0][0]
__________________________________________________________________________________________________
conv2d_69 (Conv2D) (None, 17, 17, 192) 258048 activation_68[0][0]
__________________________________________________________________________________________________
conv2d_70 (Conv2D) (None, 17, 17, 192) 147456 average_pooling2d_7[0][0]
__________________________________________________________________________________________________
batch_normalization_61 (BatchNo (None, 17, 17, 192) 576 conv2d_61[0][0]
__________________________________________________________________________________________________
batch_normalization_64 (BatchNo (None, 17, 17, 192) 576 conv2d_64[0][0]
__________________________________________________________________________________________________
batch_normalization_69 (BatchNo (None, 17, 17, 192) 576 conv2d_69[0][0]
__________________________________________________________________________________________________
batch_normalization_70 (BatchNo (None, 17, 17, 192) 576 conv2d_70[0][0]
__________________________________________________________________________________________________
activation_61 (Activation) (None, 17, 17, 192) 0 batch_normalization_61[0][0]
__________________________________________________________________________________________________
activation_64 (Activation) (None, 17, 17, 192) 0 batch_normalization_64[0][0]
__________________________________________________________________________________________________
activation_69 (Activation) (None, 17, 17, 192) 0 batch_normalization_69[0][0]
__________________________________________________________________________________________________
activation_70 (Activation) (None, 17, 17, 192) 0 batch_normalization_70[0][0]
__________________________________________________________________________________________________
mixed7 (Concatenate) (None, 17, 17, 768) 0 activation_61[0][0]
activation_64[0][0]
activation_69[0][0]
activation_70[0][0]
__________________________________________________________________________________________________
conv2d_73 (Conv2D) (None, 17, 17, 192) 147456 mixed7[0][0]
__________________________________________________________________________________________________
batch_normalization_73 (BatchNo (None, 17, 17, 192) 576 conv2d_73[0][0]
__________________________________________________________________________________________________
activation_73 (Activation) (None, 17, 17, 192) 0 batch_normalization_73[0][0]
__________________________________________________________________________________________________
conv2d_74 (Conv2D) (None, 17, 17, 192) 258048 activation_73[0][0]
__________________________________________________________________________________________________
batch_normalization_74 (BatchNo (None, 17, 17, 192) 576 conv2d_74[0][0]
__________________________________________________________________________________________________
activation_74 (Activation) (None, 17, 17, 192) 0 batch_normalization_74[0][0]
__________________________________________________________________________________________________
conv2d_71 (Conv2D) (None, 17, 17, 192) 147456 mixed7[0][0]
__________________________________________________________________________________________________
conv2d_75 (Conv2D) (None, 17, 17, 192) 258048 activation_74[0][0]
__________________________________________________________________________________________________
batch_normalization_71 (BatchNo (None, 17, 17, 192) 576 conv2d_71[0][0]
__________________________________________________________________________________________________
batch_normalization_75 (BatchNo (None, 17, 17, 192) 576 conv2d_75[0][0]
__________________________________________________________________________________________________
activation_71 (Activation) (None, 17, 17, 192) 0 batch_normalization_71[0][0]
__________________________________________________________________________________________________
activation_75 (Activation) (None, 17, 17, 192) 0 batch_normalization_75[0][0]
__________________________________________________________________________________________________
conv2d_72 (Conv2D) (None, 8, 8, 320) 552960 activation_71[0][0]
__________________________________________________________________________________________________
conv2d_76 (Conv2D) (None, 8, 8, 192) 331776 activation_75[0][0]
__________________________________________________________________________________________________
batch_normalization_72 (BatchNo (None, 8, 8, 320) 960 conv2d_72[0][0]
__________________________________________________________________________________________________
batch_normalization_76 (BatchNo (None, 8, 8, 192) 576 conv2d_76[0][0]
__________________________________________________________________________________________________
activation_72 (Activation) (None, 8, 8, 320) 0 batch_normalization_72[0][0]
__________________________________________________________________________________________________
activation_76 (Activation) (None, 8, 8, 192) 0 batch_normalization_76[0][0]
__________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D) (None, 8, 8, 768) 0 mixed7[0][0]
__________________________________________________________________________________________________
mixed8 (Concatenate) (None, 8, 8, 1280) 0 activation_72[0][0]
activation_76[0][0]
max_pooling2d_4[0][0]
__________________________________________________________________________________________________
conv2d_81 (Conv2D) (None, 8, 8, 448) 573440 mixed8[0][0]
__________________________________________________________________________________________________
batch_normalization_81 (BatchNo (None, 8, 8, 448) 1344 conv2d_81[0][0]
__________________________________________________________________________________________________
activation_81 (Activation) (None, 8, 8, 448) 0 batch_normalization_81[0][0]
__________________________________________________________________________________________________
conv2d_78 (Conv2D) (None, 8, 8, 384) 491520 mixed8[0][0]
__________________________________________________________________________________________________
conv2d_82 (Conv2D) (None, 8, 8, 384) 1548288 activation_81[0][0]
__________________________________________________________________________________________________
batch_normalization_78 (BatchNo (None, 8, 8, 384) 1152 conv2d_78[0][0]
__________________________________________________________________________________________________
batch_normalization_82 (BatchNo (None, 8, 8, 384) 1152 conv2d_82[0][0]
__________________________________________________________________________________________________
activation_78 (Activation) (None, 8, 8, 384) 0 batch_normalization_78[0][0]
__________________________________________________________________________________________________
activation_82 (Activation) (None, 8, 8, 384) 0 batch_normalization_82[0][0]
__________________________________________________________________________________________________
conv2d_79 (Conv2D) (None, 8, 8, 384) 442368 activation_78[0][0]
__________________________________________________________________________________________________
conv2d_80 (Conv2D) (None, 8, 8, 384) 442368 activation_78[0][0]
__________________________________________________________________________________________________
conv2d_83 (Conv2D) (None, 8, 8, 384) 442368 activation_82[0][0]
__________________________________________________________________________________________________
conv2d_84 (Conv2D) (None, 8, 8, 384) 442368 activation_82[0][0]
__________________________________________________________________________________________________
average_pooling2d_8 (AveragePoo (None, 8, 8, 1280) 0 mixed8[0][0]
__________________________________________________________________________________________________
conv2d_77 (Conv2D) (None, 8, 8, 320) 409600 mixed8[0][0]
__________________________________________________________________________________________________
batch_normalization_79 (BatchNo (None, 8, 8, 384) 1152 conv2d_79[0][0]
__________________________________________________________________________________________________
batch_normalization_80 (BatchNo (None, 8, 8, 384) 1152 conv2d_80[0][0]
__________________________________________________________________________________________________
batch_normalization_83 (BatchNo (None, 8, 8, 384) 1152 conv2d_83[0][0]
__________________________________________________________________________________________________
batch_normalization_84 (BatchNo (None, 8, 8, 384) 1152 conv2d_84[0][0]
__________________________________________________________________________________________________
conv2d_85 (Conv2D) (None, 8, 8, 192) 245760 average_pooling2d_8[0][0]
__________________________________________________________________________________________________
batch_normalization_77 (BatchNo (None, 8, 8, 320) 960 conv2d_77[0][0]
__________________________________________________________________________________________________
activation_79 (Activation) (None, 8, 8, 384) 0 batch_normalization_79[0][0]
__________________________________________________________________________________________________
activation_80 (Activation) (None, 8, 8, 384) 0 batch_normalization_80[0][0]
__________________________________________________________________________________________________
activation_83 (Activation) (None, 8, 8, 384) 0 batch_normalization_83[0][0]
__________________________________________________________________________________________________
activation_84 (Activation) (None, 8, 8, 384) 0 batch_normalization_84[0][0]
__________________________________________________________________________________________________
batch_normalization_85 (BatchNo (None, 8, 8, 192) 576 conv2d_85[0][0]
__________________________________________________________________________________________________
activation_77 (Activation) (None, 8, 8, 320) 0 batch_normalization_77[0][0]
__________________________________________________________________________________________________
mixed9_0 (Concatenate) (None, 8, 8, 768) 0 activation_79[0][0]
activation_80[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 8, 8, 768) 0 activation_83[0][0]
activation_84[0][0]
__________________________________________________________________________________________________
activation_85 (Activation) (None, 8, 8, 192) 0 batch_normalization_85[0][0]
__________________________________________________________________________________________________
mixed9 (Concatenate) (None, 8, 8, 2048) 0 activation_77[0][0]
mixed9_0[0][0]
concatenate_1[0][0]
activation_85[0][0]
__________________________________________________________________________________________________
conv2d_90 (Conv2D) (None, 8, 8, 448) 917504 mixed9[0][0]
__________________________________________________________________________________________________
batch_normalization_90 (BatchNo (None, 8, 8, 448) 1344 conv2d_90[0][0]
__________________________________________________________________________________________________
activation_90 (Activation) (None, 8, 8, 448) 0 batch_normalization_90[0][0]
__________________________________________________________________________________________________
conv2d_87 (Conv2D) (None, 8, 8, 384) 786432 mixed9[0][0]
__________________________________________________________________________________________________
conv2d_91 (Conv2D) (None, 8, 8, 384) 1548288 activation_90[0][0]
__________________________________________________________________________________________________
batch_normalization_87 (BatchNo (None, 8, 8, 384) 1152 conv2d_87[0][0]
__________________________________________________________________________________________________
batch_normalization_91 (BatchNo (None, 8, 8, 384) 1152 conv2d_91[0][0]
__________________________________________________________________________________________________
activation_87 (Activation) (None, 8, 8, 384) 0 batch_normalization_87[0][0]
__________________________________________________________________________________________________
activation_91 (Activation) (None, 8, 8, 384) 0 batch_normalization_91[0][0]
__________________________________________________________________________________________________
conv2d_88 (Conv2D) (None, 8, 8, 384) 442368 activation_87[0][0]
__________________________________________________________________________________________________
conv2d_89 (Conv2D) (None, 8, 8, 384) 442368 activation_87[0][0]
__________________________________________________________________________________________________
conv2d_92 (Conv2D) (None, 8, 8, 384) 442368 activation_91[0][0]
__________________________________________________________________________________________________
conv2d_93 (Conv2D) (None, 8, 8, 384) 442368 activation_91[0][0]
__________________________________________________________________________________________________
average_pooling2d_9 (AveragePoo (None, 8, 8, 2048) 0 mixed9[0][0]
__________________________________________________________________________________________________
conv2d_86 (Conv2D) (None, 8, 8, 320) 655360 mixed9[0][0]
__________________________________________________________________________________________________
batch_normalization_88 (BatchNo (None, 8, 8, 384) 1152 conv2d_88[0][0]
__________________________________________________________________________________________________
batch_normalization_89 (BatchNo (None, 8, 8, 384) 1152 conv2d_89[0][0]
__________________________________________________________________________________________________
batch_normalization_92 (BatchNo (None, 8, 8, 384) 1152 conv2d_92[0][0]
__________________________________________________________________________________________________
batch_normalization_93 (BatchNo (None, 8, 8, 384) 1152 conv2d_93[0][0]
__________________________________________________________________________________________________
conv2d_94 (Conv2D) (None, 8, 8, 192) 393216 average_pooling2d_9[0][0]
__________________________________________________________________________________________________
batch_normalization_86 (BatchNo (None, 8, 8, 320) 960 conv2d_86[0][0]
__________________________________________________________________________________________________
activation_88 (Activation) (None, 8, 8, 384) 0 batch_normalization_88[0][0]
__________________________________________________________________________________________________
activation_89 (Activation) (None, 8, 8, 384) 0 batch_normalization_89[0][0]
__________________________________________________________________________________________________
activation_92 (Activation) (None, 8, 8, 384) 0 batch_normalization_92[0][0]
__________________________________________________________________________________________________
activation_93 (Activation) (None, 8, 8, 384) 0 batch_normalization_93[0][0]
__________________________________________________________________________________________________
batch_normalization_94 (BatchNo (None, 8, 8, 192) 576 conv2d_94[0][0]
__________________________________________________________________________________________________
activation_86 (Activation) (None, 8, 8, 320) 0 batch_normalization_86[0][0]
__________________________________________________________________________________________________
mixed9_1 (Concatenate) (None, 8, 8, 768) 0 activation_88[0][0]
activation_89[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, 8, 8, 768) 0 activation_92[0][0]
activation_93[0][0]
__________________________________________________________________________________________________
activation_94 (Activation) (None, 8, 8, 192) 0 batch_normalization_94[0][0]
__________________________________________________________________________________________________
mixed10 (Concatenate) (None, 8, 8, 2048) 0 activation_86[0][0]
mixed9_1[0][0]
concatenate_2[0][0]
activation_94[0][0]
__________________________________________________________________________________________________
avg_pool (GlobalAveragePooling2 (None, 2048) 0 mixed10[0][0]
__________________________________________________________________________________________________
predictions (Dense) (None, 1000) 2049000 avg_pool[0][0]
==================================================================================================
Total params: 23,851,784
Trainable params: 23,817,352
Non-trainable params: 34,432
__________________________________________________________________________________________________
None
Ejemplos¶
In [4]:
from keras.preprocessing import image
x_img = image.load_img("./images/cat3.jpg", target_size=(299,299))
plt.imshow(x_img)
plt.show()
In [6]:
x = image.img_to_array(x_img)
# Cambio de rango 0-255 -> -1-1
x /= 255
x -= 0.5
x *= 2
x = x.reshape([1, x.shape[0], x.shape[1], x.shape[2]])
y = iv3.predict(x)
decode_predictions(y)
print(y)
[[1.85478038e-05 2.02695428e-05 7.13076679e-06 2.26234188e-05 2.22623967e-05 5.16077380e-05 1.89692091e-05 1.14090481e-05 1.42797089e-05 1.84101536e-05 4.49391428e-06 9.93728281e-06 4.21596269e-05 4.51507549e-06 8.58717613e-06 7.15832502e-05 1.86791622e-05 4.80192011e-06 4.86571116e-06 3.28097713e-06 1.13314518e-05 2.43053892e-05 3.47499117e-05 2.02119027e-05 2.21110258e-05 2.76289538e-05 2.46968539e-05 2.42523183e-05 9.97544430e-06 2.01326839e-05 6.86222702e-05 2.68340973e-05 1.77357269e-05 1.64538324e-05 4.53044322e-06 2.36808010e-05 1.61464086e-05 3.93880555e-06 1.17049503e-05 8.97241807e-06 1.30215294e-05 8.89187595e-06 1.53154378e-05 9.70287510e-06 1.89530274e-05 8.05237778e-06 1.26600407e-05 1.21466055e-05 9.52915252e-06 4.92540448e-06 7.03208161e-06 1.56023671e-05 1.87626101e-05 1.71548472e-05 1.03116190e-05 2.99454459e-05 1.40531101e-05 9.76621686e-06 4.11087603e-05 1.46527627e-05 1.82162094e-05 1.36437648e-05 6.61170816e-06 5.58167812e-06 1.47060309e-05 1.91383642e-05 1.64175308e-05 2.47794505e-05 1.23000800e-05 8.09520407e-06 2.92375644e-05 9.43773557e-06 7.16490513e-06 9.39307301e-06 5.27794327e-06 3.86937172e-05 1.37629086e-05 1.31590496e-05 1.80626612e-05 2.86721970e-05 1.96884903e-05 4.59152125e-06 2.43982358e-05 9.81698759e-06 1.24147355e-05 3.28642591e-06 2.88504016e-05 6.27915779e-06 1.56361057e-05 1.22243728e-05 2.94278434e-05 2.40596164e-05 1.65533966e-05 8.18395165e-06 5.42164162e-06 1.92823245e-05 1.73006265e-05 1.51968452e-05 1.65497368e-05 6.84125771e-05 2.29653124e-05 2.24007199e-05 4.10285656e-06 2.97742154e-05 6.50159063e-06 5.23403878e-06 5.60267426e-06 1.15921193e-05 1.93809756e-05 1.26327477e-05 7.66372395e-05 2.82962992e-05 2.81292923e-05 6.59698253e-06 3.28182068e-05 2.36710930e-05 1.07556534e-05 1.53223191e-05 1.04776909e-05 1.00754232e-05 1.23132486e-05 4.21581990e-06 1.37090701e-05 1.18774451e-05 8.16244665e-06 7.18046704e-06 2.63863949e-05 1.14320173e-05 1.12955340e-05 5.49260085e-06 3.35288678e-05 1.01105452e-05 1.60271593e-05 6.51130467e-05 2.27512010e-05 1.38011565e-05 3.55573357e-05 1.30875123e-05 1.15952034e-05 1.59794345e-05 4.93606058e-06 9.64844276e-06 1.52713474e-05 1.41323098e-05 5.95552501e-06 3.20347463e-05 1.44187443e-05 2.03752843e-05 8.40683424e-06 2.06355471e-05 2.47528551e-05 1.20181858e-05 1.24530006e-05 3.41506825e-06 8.88624800e-06 9.35298249e-06 3.34722172e-05 3.02144804e-06 6.15434374e-06 1.03509710e-05 2.31576851e-05 1.29794989e-05 1.03453467e-05 2.52177488e-05 1.53871151e-05 6.71174212e-06 8.36868549e-06 1.13990636e-05 1.30372728e-05 6.42960276e-06 8.13857605e-06 8.75258502e-06 1.00798434e-05 4.23306956e-05 8.47852425e-06 9.41510825e-06 1.06278121e-05 6.06349658e-06 2.60087927e-05 2.54361184e-05 6.22060688e-05 9.96529889e-06 2.11381448e-05 1.05261561e-05 3.13819291e-05 2.68118420e-05 2.26887551e-05 1.25682782e-05 1.32098348e-05 6.71044036e-05 1.13628112e-05 1.57546237e-05 3.27696785e-06 1.66117225e-05 5.90112541e-06 1.59864467e-05 1.70110434e-05 1.97039517e-05 5.84357713e-06 1.69495888e-05 4.37139570e-05 4.43091403e-06 2.57122301e-05 7.29528847e-06 1.14226350e-05 1.49247871e-05 1.60112268e-05 3.76466087e-05 2.55400810e-05 1.68450042e-05 8.26480846e-06 1.66537720e-05 5.48638718e-06 9.95244773e-06 1.33097592e-05 1.39556796e-05 2.49984132e-05 1.21746698e-05 4.20423894e-05 8.83956163e-05 2.13508156e-05 9.74684463e-06 6.76959053e-06 9.77709169e-06 1.78593073e-05 2.71272802e-06 1.05084127e-05 6.24027098e-06 5.65255414e-06 8.33931972e-06 3.01538557e-06 1.18561802e-05 1.46131724e-05 1.59337214e-05 1.87265723e-05 1.72330965e-05 1.60078816e-05 9.84463531e-06 1.32707855e-05 2.50731227e-05 1.67728049e-05 2.10599319e-05 2.86648410e-05 3.12107004e-05 1.83054563e-05 2.13778112e-05 1.41761675e-05 1.89382827e-05 5.57982894e-05 7.64508331e-06 1.54608970e-05 1.46184548e-05 6.47369279e-06 1.77619313e-05 3.34798324e-06 1.72588188e-05 2.59080625e-05 6.95134167e-06 1.01222677e-05 1.54643476e-05 9.65035142e-05 3.34812717e-06 8.44637179e-06 7.51677999e-06 6.73310387e-06 3.11948970e-05 2.33909650e-05 1.88828617e-05 1.00124807e-05 9.02228658e-06 1.08610266e-05 3.89581419e-06 1.21624280e-05 2.26001939e-05 1.06672605e-05 4.46866079e-06 1.21470921e-05 7.81184826e-06 1.01300611e-05 7.94784046e-06 1.38726664e-05 8.46829489e-02 8.73430490e-01 9.94855771e-04 6.36936966e-05 6.56249933e-03 1.69466621e-05 7.07566040e-04 1.24260941e-05 1.55803464e-05 1.35414484e-05 5.28092132e-05 1.13802998e-04 3.47763216e-06 1.62551150e-05 9.80279583e-06 8.46759121e-06 9.37503137e-06 2.08054280e-05 3.25174115e-05 2.22116068e-05 3.84157102e-05 4.96461071e-05 5.61225570e-05 1.55978287e-05 3.05119065e-05 2.87882849e-05 1.56314982e-05 2.25197491e-05 6.65999369e-06 2.60435663e-05 1.64502380e-05 2.85962542e-05 1.38511832e-05 1.46692473e-05 5.63961839e-05 2.60482611e-05 3.00633965e-05 1.35368264e-05 1.66671507e-05 1.96386463e-05 1.34821985e-05 2.50580633e-05 3.00391803e-05 1.19286642e-05 3.80925194e-05 1.36894078e-05 1.77560869e-05 1.11765185e-05 2.36023625e-05 2.99147359e-05 3.39746002e-05 2.34151375e-05 2.19500052e-05 7.40017276e-06 9.31303566e-06 3.71491319e-06 1.16432066e-05 1.52093726e-05 2.99403346e-05 2.51710644e-05 7.10298718e-06 1.07055494e-05 9.97556799e-06 1.07770193e-05 3.36006306e-05 1.16949423e-05 1.22781521e-05 1.51170989e-05 1.46515040e-05 1.44478554e-05 1.12435482e-05 1.74047018e-05 1.34257325e-05 6.20702713e-06 1.65000474e-05 1.03918710e-05 3.55183220e-05 1.35005985e-05 1.03467482e-05 2.35696134e-05 5.27663451e-06 4.43055478e-06 8.91677428e-06 6.87240708e-06 9.70607744e-06 7.12102110e-06 1.53648998e-05 1.86909947e-05 9.82724396e-06 7.33008847e-06 6.86209933e-06 7.47341483e-06 1.01772766e-05 8.45744671e-06 5.76292541e-06 1.12193520e-05 1.31111110e-05 9.04137414e-06 1.94651075e-05 7.96125187e-06 5.14340218e-06 8.49440312e-06 1.11217805e-05 1.02225677e-05 1.23385453e-05 1.73892895e-05 2.86477880e-05 5.95224310e-06 1.82800759e-05 8.78100309e-06 1.76722624e-05 1.28241118e-05 1.20329460e-05 2.02238771e-05 1.31151874e-05 1.08137847e-05 7.92078117e-06 2.99697604e-05 1.61211137e-05 8.34189632e-06 1.09835009e-05 8.03372022e-05 1.39339372e-05 2.94152160e-05 2.30078658e-05 1.25164142e-05 5.97656481e-06 1.47976862e-05 6.24364020e-06 1.46538796e-05 2.01219736e-05 3.46592606e-05 1.50174483e-05 1.54716559e-04 3.70463590e-06 3.45251610e-05 2.37096592e-05 7.48203965e-05 4.93621374e-05 2.45275987e-05 4.24492609e-05 1.78312785e-05 3.73758776e-05 2.94590118e-05 1.13554024e-05 1.33960739e-05 5.42601720e-06 6.24510867e-05 1.72964192e-05 7.42178554e-06 2.77734616e-05 1.97078407e-05 2.48200595e-05 4.18007112e-05 5.36413223e-04 9.17528087e-06 1.68541483e-05 2.86769828e-05 1.50144697e-05 2.13468848e-05 1.24674389e-05 1.10009332e-05 9.84789876e-05 1.55250527e-05 1.01449687e-05 8.13477818e-05 1.43690659e-05 1.54824102e-05 3.48021167e-05 1.86924208e-05 5.46196825e-05 7.51488778e-06 9.17273937e-05 4.72283427e-05 3.30815346e-05 4.71644307e-05 8.83021712e-05 2.04537628e-05 9.94564034e-05 1.83096308e-05 1.05187501e-05 1.23981648e-04 1.49645144e-04 4.95405911e-05 1.89983039e-05 1.53445599e-05 8.79053823e-06 1.31192910e-05 4.04995371e-05 1.53782839e-05 2.00390714e-05 3.19887695e-05 3.88133158e-05 1.17089812e-05 4.81674651e-05 9.10869039e-06 1.49465232e-05 6.29173359e-04 4.15495342e-05 1.01636106e-05 2.15739583e-05 1.35866330e-05 1.24788703e-05 1.73687531e-05 6.09270501e-06 2.44063576e-05 1.22874071e-05 2.29935831e-05 3.72090399e-05 4.65895437e-06 1.44740316e-05 2.84070993e-05 2.03400668e-05 1.90823757e-05 1.35723358e-05 3.16396799e-05 1.12747939e-05 8.50894958e-06 9.54895768e-06 7.40946643e-06 8.25736424e-06 7.20483877e-06 2.98625127e-05 2.27804248e-05 1.36200779e-05 1.30952658e-05 1.36420467e-05 9.53824128e-05 4.79595246e-06 1.17117725e-05 8.53792790e-06 2.04279168e-05 1.54175941e-05 3.58359393e-05 5.64065631e-05 5.59080727e-05 1.18057687e-05 1.56085425e-05 9.00789091e-05 1.29495966e-04 6.95474664e-05 2.18727946e-05 2.08336387e-05 3.09822608e-05 1.16835045e-05 5.85654525e-05 1.47616165e-05 2.10900598e-05 1.92766420e-05 1.26674677e-05 2.11542774e-05 6.38453057e-05 9.81495577e-06 1.41529381e-04 1.57035774e-05 1.98489470e-05 1.05536665e-05 1.28255551e-05 1.40101343e-04 8.05806212e-06 2.44389648e-05 2.09702164e-04 4.96419889e-05 1.59268875e-05 3.27997768e-05 1.61062526e-05 8.72673900e-06 1.42186400e-05 8.47873889e-05 8.48398304e-06 9.89807450e-05 8.88822688e-05 6.42560335e-05 5.74411570e-06 9.39575148e-06 4.33229652e-05 1.91261206e-05 1.48404933e-05 7.58488459e-05 1.36002582e-05 9.18208298e-06 3.67511748e-05 1.99931037e-05 1.06255002e-05 1.91819818e-05 4.16757612e-05 3.22766100e-05 2.02136962e-05 1.19301312e-05 2.34710296e-05 1.15294288e-05 1.82220465e-05 1.33067379e-05 3.75279596e-05 8.10204710e-06 2.50327430e-05 2.67719024e-05 1.21194716e-05 1.86195994e-05 5.46601696e-05 1.97279242e-05 1.36868102e-05 1.03881357e-05 2.83567006e-05 1.59961783e-05 1.72396212e-05 2.63897819e-04 4.15898889e-04 9.12451796e-05 9.13109398e-06 3.22796841e-05 3.88253466e-05 3.71831811e-05 1.09184257e-05 1.04374094e-05 5.27913398e-05 1.76385365e-05 6.15068802e-06 2.44017046e-05 2.37185486e-05 7.33308116e-06 7.33954585e-06 1.53880828e-05 2.18426321e-05 1.44093965e-05 7.11763787e-05 6.69123983e-06 3.31673291e-05 6.04596562e-06 1.01703547e-04 4.24198806e-05 3.26368354e-05 1.00326579e-05 2.98963114e-05 1.97444297e-05 1.37274656e-05 8.45670547e-06 7.48270395e-05 2.55101168e-05 1.17950716e-04 1.42365643e-05 2.20339105e-04 3.45523040e-05 3.82407743e-05 9.93167396e-06 1.72580949e-05 4.14375199e-06 1.27390294e-05 1.34486327e-05 3.18492312e-05 1.46003003e-05 1.57743489e-05 2.95901100e-05 1.36611325e-05 1.90476676e-05 2.09157806e-05 2.39315123e-05 1.45133135e-05 2.03429772e-05 1.61226508e-05 5.94486255e-06 2.27043838e-05 9.32889543e-06 2.00017821e-05 1.07702281e-05 1.06337320e-05 2.89557247e-05 5.04575983e-05 1.68724018e-05 9.07131016e-06 3.00217671e-05 2.73155838e-05 1.63690511e-05 7.64652759e-06 1.20151726e-05 1.47765750e-05 3.48740141e-05 8.36897289e-06 1.09844914e-04 1.77908878e-05 1.40958737e-05 5.64842449e-05 4.96818939e-06 5.45010480e-05 1.46396042e-05 1.73499975e-05 1.58250987e-05 5.25435917e-06 4.94840242e-05 8.01123388e-05 1.84013570e-05 3.74929368e-05 9.23407424e-05 4.51307133e-05 1.08769331e-04 5.48800163e-05 2.97009337e-05 1.24872267e-05 2.70874316e-05 2.39480869e-05 3.81067985e-05 4.08942433e-05 1.23593672e-05 5.66095923e-06 9.20315779e-06 2.46543095e-05 1.02053546e-05 1.78781738e-05 1.44080095e-05 1.32845871e-05 1.16498704e-05 8.44773822e-05 3.41749728e-05 3.26344998e-05 2.28782446e-05 6.59257057e-05 4.93567713e-05 1.08596278e-05 1.19679307e-05 3.96624528e-05 1.71758347e-05 1.98076305e-05 1.83783122e-05 2.64519531e-05 1.55850121e-05 1.39364220e-05 3.84702616e-05 6.46049812e-05 1.46794346e-05 1.74842899e-05 1.70501680e-05 2.78183124e-05 3.17408885e-05 3.28999631e-05 3.06031507e-05 1.43116795e-05 1.30674580e-05 1.83311568e-05 2.93582143e-05 3.41188970e-05 7.21030665e-05 1.01572783e-04 8.72760866e-05 1.92586158e-05 2.25065014e-05 1.45958738e-05 2.97206552e-05 1.93899439e-03 1.71688916e-05 1.49542366e-05 8.61846347e-05 2.42186907e-05 5.78088402e-05 6.19326784e-06 3.77262336e-06 2.40950467e-05 1.05073996e-05 3.13451419e-05 1.21923094e-05 1.87958685e-05 1.89877810e-05 1.01162317e-04 1.25206880e-05 7.32552944e-05 5.71982264e-06 6.42162377e-06 6.99230877e-05 2.44605581e-05 9.87856765e-06 1.71319509e-04 3.20519503e-05 3.58974394e-05 3.51155904e-04 2.08159454e-05 1.16996152e-05 3.44552645e-05 9.51674701e-06 4.65718877e-05 2.05205051e-05 2.76178762e-04 1.27397288e-04 3.63459662e-06 3.26177615e-05 2.64209666e-05 4.41921402e-05 7.42065276e-06 7.11801113e-05 6.89606850e-06 8.45436298e-05 2.26369066e-05 1.37212210e-05 5.21365582e-05 3.13151259e-06 4.00511453e-05 1.96277124e-05 1.69967880e-05 3.10145733e-05 3.80545389e-05 1.36021008e-05 2.16136195e-05 8.08650020e-06 1.87429643e-04 2.54713177e-05 3.86506763e-05 1.71174052e-05 4.25714934e-05 7.35465255e-06 1.99414626e-05 1.76798658e-05 1.06868480e-04 4.78456377e-06 3.95998322e-05 7.05021594e-05 6.72590337e-04 1.94237746e-05 6.68127950e-06 1.27911771e-04 1.22660385e-05 3.59425940e-05 3.76585081e-06 2.18644327e-05 2.11236347e-05 1.90328319e-05 3.83614570e-05 1.65682122e-05 2.22842118e-05 1.53541332e-05 1.18096195e-05 3.73223047e-05 1.30899334e-05 3.84999759e-04 3.47354689e-05 4.00743011e-05 1.45989779e-05 1.67850940e-05 4.58711547e-05 2.99505300e-05 4.45389960e-05 2.90136340e-05 1.96821456e-05 4.41544762e-05 2.01368875e-05 3.68377405e-05 3.09058737e-06 1.30118833e-05 1.86129055e-05 4.91822138e-05 6.90000670e-05 9.90597346e-06 8.02729846e-06 5.15164247e-05 1.12541156e-05 1.31154002e-05 1.71333140e-05 3.43077809e-05 1.39905067e-04 4.42223711e-04 2.47680400e-05 1.73887238e-05 3.18361272e-04 1.92450862e-05 8.27392523e-06 1.55158305e-05 1.18080088e-05 7.02156467e-05 3.06145375e-05 1.67080525e-05 1.37208690e-05 1.97858426e-05 4.72318134e-05 3.66524037e-05 8.61281005e-05 1.42563331e-05 1.29380851e-05 2.34373238e-05 1.33929561e-05 1.35833297e-05 2.05815177e-05 5.12186270e-05 2.17346624e-05 1.39064228e-04 9.86686427e-06 1.03126522e-05 1.84693963e-05 1.46838738e-05 1.56951046e-05 2.50473804e-05 1.10784531e-05 1.30940170e-05 1.42753379e-05 3.47411660e-05 5.10432765e-05 3.56286146e-05 1.36373001e-05 2.08703095e-05 8.45942355e-04 2.40834670e-05 1.07571914e-05 1.34390226e-04 2.16999306e-05 1.99107926e-05 5.16941836e-05 5.01799477e-05 1.35228920e-05 4.64425138e-05 4.11228530e-06 8.57409850e-06 1.32499945e-05 3.19325627e-05 8.17710952e-06 2.20328056e-05 2.82994843e-05 1.20381455e-05 1.06518448e-04 1.26259065e-05 5.71134209e-04 1.05644969e-04 4.09959248e-05 9.97205352e-05 2.97064016e-05 3.27459566e-05 7.82607240e-05 3.67349530e-05 4.56705427e-04 7.47805971e-05 1.69979721e-05 2.35913794e-05 7.31647697e-06 1.81319469e-04 2.09251775e-05 6.41048791e-06 1.90041574e-05 6.06038566e-06 3.72443646e-05 3.08806120e-05 4.88145051e-05 1.74781708e-05 1.57535804e-04 2.95191094e-05 2.13877029e-05 2.31890681e-05 1.80869483e-05 1.07390069e-05 2.47195949e-05 1.29029922e-05 1.58438379e-05 9.08495440e-06 8.50327797e-06 3.65845117e-05 3.24900102e-05 1.43441312e-05 1.54318477e-05 9.79750530e-06 2.00246086e-05 5.98498400e-06 4.31518420e-05 2.12413270e-05 1.65851907e-05 2.17708759e-04 1.67415390e-04 5.97952167e-05 1.09810026e-04 1.05375708e-04 6.97242831e-06 1.17399195e-05 1.26963614e-05 3.40658335e-05 2.76527062e-05 1.21482735e-05 1.90749161e-05 5.30482212e-05 3.38100144e-05 1.18348753e-05 1.17838899e-05 7.99258669e-06 8.37443349e-06 1.05409317e-05 1.88508875e-05 2.58603941e-05 5.47884747e-06 4.67075988e-05 1.64339326e-05 1.27771245e-05 2.35247007e-05 1.01208389e-05 8.37336393e-06 1.75506302e-05 9.92239220e-05 5.40963083e-05 1.33763360e-05 8.69524156e-05 8.42604913e-06 1.42586578e-05 2.96406070e-05 5.75680424e-05 9.61572277e-06 3.70015005e-05 1.20788791e-05 2.08280180e-05 1.79631825e-05 2.34888303e-05 1.57847771e-05 2.38212142e-05 7.01793615e-05 3.27503913e-05 1.04387136e-05 3.18343809e-05 8.30267345e-06 8.56367205e-06 1.79271738e-05 9.34311356e-06 9.34749914e-06 1.33040367e-05 1.07808437e-05 7.44436966e-06 8.16746160e-06 1.40291659e-05 2.49008135e-05 7.34691057e-05]]
In [7]:
x_img = image.load_img("./images/beer1.jpg", target_size=(299,299))
plt.imshow(x_img)
plt.show()
x = image.img_to_array(x_img)
# Cambio de rango 0-255 -> -1-1
x /= 255
x -= 0.5
x *= 2
x = x.reshape([1, x.shape[0], x.shape[1], x.shape[2]])
y = iv3.predict(x)
decode_predictions(y)
Out[7]:
[[('n02823750', 'beer_glass', 0.99242014),
('n03026506', 'Christmas_stocking', 0.0011027737),
('n02823428', 'beer_bottle', 0.0006102637),
('n07584110', 'consomme', 9.935716e-05),
('n07615774', 'ice_lolly', 6.335112e-05)]]
In [8]:
x_img = image.load_img("./img/sacks_book.jpg", target_size=(299,299))
plt.imshow(x_img)
plt.show()
x = image.img_to_array(x_img)
# Cambio de rango 0-255 -> -1-1
x /= 255
x -= 0.5
x *= 2
x = x.reshape([1, x.shape[0], x.shape[1], x.shape[2]])
y = iv3.predict(x)
decode_predictions(y)
Out[8]:
[[('n07248320', 'book_jacket', 0.9367033),
('n04423845', 'thimble', 0.009080168),
('n03843555', 'oil_filter', 0.007826038),
('n03690938', 'lotion', 0.0058158645),
('n03916031', 'perfume', 0.0033939623)]]
In [28]:
x_img = image.load_img("./images/beer1.jpg", target_size=(299,299))
plt.imshow(x_img)
plt.show()
x = image.img_to_array(x_img)
# Cambio de rango 0-255 -> -1-1
x /= 255
x -= 0.5
x *= 2
x = x.reshape([1, x.shape[0], x.shape[1], x.shape[2]])
y = iv3.predict(x)
decode_predictions(y)
Out[28]:
[[('n02823750', 'beer_glass', 0.99242014),
('n03026506', 'Christmas_stocking', 0.0011027737),
('n02823428', 'beer_bottle', 0.0006102637),
('n07584110', 'consomme', 9.935716e-05),
('n07615774', 'ice_lolly', 6.335112e-05)]]
Ataque Adversario:¶
In [29]:
%%time
# punto de entrada de capa 0:
inp_layer = iv3.layers[0].input
# punto de salida de todo el grafo (última capa):
out_layer = iv3.layers[-1].output
print(inp_layer)
print(out_layer)
target_class = 808 # Sombrero!
# maximiza la probabilidad de la clase
loss = out_layer[0, target_class]
print(loss)
# Proceso gradiente del costo con respecto a la entrada
# K.gradients(salida de funcion a minimisar, parametros a ajustar)
grad = K.gradients(loss, inp_layer)[0]
print(grad)
# K.function instancia una funcion Keras
# K.function([placeholder tensors (input)], [output tensors])
optimize_gradient = K.function([inp_layer, K.learning_phase()], [grad, loss])
adv = np.copy(x)
cost = 0.0
while cost < 0.95:
gr, cost = optimize_gradient([adv, 0]) #0 is test 1 is train
adv += gr
print("Target cost:", cost)
Tensor("input_1:0", shape=(?, 299, 299, 3), dtype=float32)
Tensor("predictions/Softmax:0", shape=(?, 1000), dtype=float32)
Tensor("strided_slice_5:0", shape=(), dtype=float32)
Tensor("gradients_5/conv2d_1/convolution_grad/Conv2DBackpropInput:0", shape=(?, 299, 299, 3), dtype=float32)
Target cost: 5.99138e-06
Target cost: 5.996031e-06
Target cost: 6.000674e-06
Target cost: 6.0053208e-06
Target cost: 6.009972e-06
Target cost: 6.014591e-06
Target cost: 6.019261e-06
Target cost: 6.0239336e-06
Target cost: 6.0285747e-06
Target cost: 6.0332045e-06
Target cost: 6.037847e-06
Target cost: 6.042481e-06
Target cost: 6.0471425e-06
Target cost: 6.051797e-06
Target cost: 6.05646e-06
Target cost: 6.0610982e-06
Target cost: 6.06577e-06
Target cost: 6.070419e-06
Target cost: 6.075115e-06
Target cost: 6.0798225e-06
Target cost: 6.0845373e-06
Target cost: 6.0892557e-06
Target cost: 6.093958e-06
Target cost: 6.0986936e-06
Target cost: 6.10344e-06
Target cost: 6.10816e-06
Target cost: 6.112872e-06
Target cost: 6.1175992e-06
Target cost: 6.1223714e-06
Target cost: 6.1271166e-06
Target cost: 6.1318838e-06
Target cost: 6.136643e-06
Target cost: 6.141423e-06
Target cost: 6.146202e-06
Target cost: 6.151002e-06
Target cost: 6.1557776e-06
Target cost: 6.1605942e-06
Target cost: 6.16543e-06
Target cost: 6.170267e-06
Target cost: 6.1750966e-06
Target cost: 6.1799483e-06
Target cost: 6.1847963e-06
Target cost: 6.18965e-06
Target cost: 6.194535e-06
Target cost: 6.199456e-06
Target cost: 6.2043787e-06
Target cost: 6.209325e-06
Target cost: 6.2142253e-06
Target cost: 6.2192203e-06
Target cost: 6.2242e-06
Target cost: 6.2292143e-06
Target cost: 6.234196e-06
Target cost: 6.2391737e-06
Target cost: 6.244205e-06
Target cost: 6.249187e-06
Target cost: 6.2541853e-06
Target cost: 6.2591935e-06
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CPU times: user 15min 42s, sys: 37 s, total: 16min 19s
Wall time: 2min 29s
In [31]:
decode_predictions(iv3.predict(adv))
Out[31]:
[[('n04259630', 'sombrero', 0.9952867),
('n03124170', 'cowboy_hat', 0.003838409),
('n03729826', 'matchstick', 0.00028981196),
('n03250847', 'drumstick', 7.400229e-05),
('n13040303', 'stinkhorn', 4.216431e-05)]]
In [32]:
adv_img = np.copy(adv)
adv_img = adv_img[0]
adv_img /= 2
adv_img += 0.5
adv_img *= 255
adv_img = adv_img.astype(np.uint8)
fig1 = plt.figure(figsize=(15,15))
ax1 = fig1.add_subplot(1,2,1)
ax1.imshow(x_img)
ax1.set_title("original")
ax1 = fig1.add_subplot(1,2,2)
ax1.imshow(adv_img)
ax1.set_title("resultado")
plt.show()
In [33]:
%%time
inp_layer = iv3.layers[0].input
out_layer = iv3.layers[-1].output
print(inp_layer)
print(out_layer)
target_class = 808
# maximiza la probabilidad de la clase
loss = out_layer[0, target_class]
print(loss)
# gradientes sobre desde la entrada a la salida
grad = K.gradients(loss, inp_layer)[0]
print(grad)
optimize_gradient = K.function([inp_layer, K.learning_phase()], [grad, loss])
adv = np.copy(x)
pert = 0.01
max_pert = x + pert
min_pert = x - pert
cost = 0.0
while cost < 0.95:
gr, cost = optimize_gradient([adv, 0])
adv += gr
adv = np.clip(adv, min_pert, max_pert)
adv = np.clip(adv, -1, 1)
print("Target cost:", cost)
Tensor("input_1:0", shape=(?, 299, 299, 3), dtype=float32)
Tensor("predictions/Softmax:0", shape=(?, 1000), dtype=float32)
Tensor("strided_slice_6:0", shape=(), dtype=float32)
Tensor("gradients_6/conv2d_1/convolution_grad/Conv2DBackpropInput:0", shape=(?, 299, 299, 3), dtype=float32)
Target cost: 5.99138e-06
Target cost: 5.9958725e-06
Target cost: 6.0003554e-06
Target cost: 6.0048374e-06
Target cost: 6.009311e-06
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Target cost: 0.0012835541
Target cost: 0.0013034467
Target cost: 0.0013238348
Target cost: 0.0013450484
Target cost: 0.0013677839
Target cost: 0.0013915154
Target cost: 0.0014155468
Target cost: 0.0014404261
Target cost: 0.0014669739
Target cost: 0.001494366
Target cost: 0.0015222387
Target cost: 0.0015515737
Target cost: 0.0015817729
Target cost: 0.0016131115
Target cost: 0.001646546
Target cost: 0.001682284
Target cost: 0.0017189104
Target cost: 0.0017564232
Target cost: 0.0017942821
Target cost: 0.0018330979
Target cost: 0.0018740932
Target cost: 0.0019161511
Target cost: 0.0019599614
Target cost: 0.0020065964
Target cost: 0.0020536005
Target cost: 0.0021003603
Target cost: 0.002149247
Target cost: 0.0022013804
Target cost: 0.0022635257
Target cost: 0.0023275446
Target cost: 0.0023922636
Target cost: 0.002456463
Target cost: 0.002526685
Target cost: 0.00261028
Target cost: 0.0027003046
Target cost: 0.0028049478
Target cost: 0.0029159184
Target cost: 0.0030298997
Target cost: 0.003148505
Target cost: 0.003275884
Target cost: 0.0034141277
Target cost: 0.0035577277
Target cost: 0.0037045276
Target cost: 0.003859892
Target cost: 0.0040412396
Target cost: 0.004246082
Target cost: 0.0044655516
Target cost: 0.004694602
Target cost: 0.004937795
Target cost: 0.0052067987
Target cost: 0.005502085
Target cost: 0.0058242944
Target cost: 0.00618323
Target cost: 0.0065969634
Target cost: 0.007131513
Target cost: 0.007978159
Target cost: 0.008996743
Target cost: 0.010462263
Target cost: 0.012339956
Target cost: 0.01499301
Target cost: 0.019216716
Target cost: 0.028321793
Target cost: 0.046433397
Target cost: 0.06309744
Target cost: 0.02576327
Target cost: 0.14590646
Target cost: 0.08092658
Target cost: 0.19062787
Target cost: 0.007676549
Target cost: 0.117401704
Target cost: 0.6763838
Target cost: 0.12830186
Target cost: 0.6906874
Target cost: 0.8274972
Target cost: 0.9301364
Target cost: 0.98990625
CPU times: user 16min 38s, sys: 38.7 s, total: 17min 17s
Wall time: 2min 38s
In [34]:
adv_img2 = np.copy(adv)
adv_img2 = adv_img2[0]
adv_img2 /= 2
adv_img2 += 0.5
adv_img2 *= 255
adv_img2 = adv_img2.astype(np.uint8)
fig1 = plt.figure(figsize=(15,15))
ax1 = fig1.add_subplot(1,2,1)
ax1.imshow(x_img)
ax1.set_title("original")
ax1 = fig1.add_subplot(1,2,2)
ax1.imshow(adv_img2)
ax1.set_title("resultado")
plt.show()
decode_predictions(iv3.predict(adv))
Out[34]:
[[('n04259630', 'sombrero', 0.99487805),
('n03124170', 'cowboy_hat', 0.0044034375),
('n03729826', 'matchstick', 0.0003975032),
('n04599235', 'wool', 9.644292e-06),
('n03250847', 'drumstick', 7.835843e-06)]]
In [17]:
from PIL import Image
im = Image.fromarray(adv_img2)
im.save("./images/kuka_hat.jpg") # con jpg anda mas o menos
In [18]:
x_img = image.load_img("./images/kuka_hat.jpg", target_size=(299,299))
x = image.img_to_array(x_img)
# Cambio de rango 0-255 -> -1-1
x /= 255
x -= 0.5
x *= 2
x = x.reshape([1, x.shape[0], x.shape[1], x.shape[2]])
y = iv3.predict(x)
plt.imshow(x_img)
plt.show()
decode_predictions(y)
Out[18]:
[[('n04599235', 'wool', 0.2589121),
('n03980874', 'poncho', 0.12783855),
('n03045698', 'cloak', 0.12625077),
('n02963159', 'cardigan', 0.061921082),
('n04325704', 'stole', 0.04588114)]]
Otro ejemplo:¶
In [19]:
x_img = image.load_img("./images/macri.jpg", target_size=(299,299))
plt.imshow(x_img)
plt.show()
x = image.img_to_array(x_img)
# Cambio de rango 0-255 -> -1-1
x /= 255
x -= 0.5
x *= 2
x = x.reshape([1, x.shape[0], x.shape[1], x.shape[2]])
y = iv3.predict(x)
decode_predictions(y)
Out[19]:
[[('n04350905', 'suit', 0.85482717),
('n04591157', 'Windsor_tie', 0.08727306),
('n02883205', 'bow_tie', 0.011159425),
('n10148035', 'groom', 0.003986692),
('n03838899', 'oboe', 0.0025524923)]]
In [20]:
%%time
inp_layer = iv3.layers[0].input
out_layer = iv3.layers[-1].output
print(inp_layer)
print(out_layer)
target_class = 283
# maximiza la probabilidad de la clase
loss = out_layer[0, target_class]
print(loss)
# gradientes sobre desde la entrada a la salida
grad = K.gradients(loss, inp_layer)[0]
print(grad)
optimize_gradient = K.function([inp_layer, K.learning_phase()], [grad, loss])
adv = np.copy(x)
pert = 0.01
max_pert = x + pert
min_pert = x - pert
cost = 0.0
while cost < 0.95:
gr, cost = optimize_gradient([adv, 0])
adv += gr
adv = np.clip(adv, min_pert, max_pert)
adv = np.clip(adv, -1, 1)
print("Target cost:", cost)
Tensor("input_1:0", shape=(?, 299, 299, 3), dtype=float32)
Tensor("predictions/Softmax:0", shape=(?, 1000), dtype=float32)
Tensor("strided_slice_2:0", shape=(), dtype=float32)
Tensor("gradients_2/conv2d_1/convolution_grad/Conv2DBackpropInput:0", shape=(?, 299, 299, 3), dtype=float32)
Target cost: 1.9830859e-05
Target cost: 1.9949837e-05
Target cost: 2.0070202e-05
Target cost: 2.019176e-05
Target cost: 2.0313966e-05
Target cost: 2.0435722e-05
Target cost: 2.055871e-05
Target cost: 2.0682339e-05
Target cost: 2.0807143e-05
Target cost: 2.0934063e-05
Target cost: 2.1061865e-05
Target cost: 2.1191216e-05
Target cost: 2.1321426e-05
Target cost: 2.1453669e-05
Target cost: 2.1587151e-05
Target cost: 2.1721802e-05
Target cost: 2.1858465e-05
Target cost: 2.1997888e-05
Target cost: 2.21381e-05
Target cost: 2.227779e-05
Target cost: 2.2417555e-05
Target cost: 2.2557371e-05
Target cost: 2.2698196e-05
Target cost: 2.2841978e-05
Target cost: 2.2988803e-05
Target cost: 2.3137916e-05
Target cost: 2.3287705e-05
Target cost: 2.3438177e-05
Target cost: 2.3588778e-05
Target cost: 2.3742343e-05
Target cost: 2.3895165e-05
Target cost: 2.4049361e-05
Target cost: 2.4205063e-05
Target cost: 2.4362103e-05
Target cost: 2.4519646e-05
Target cost: 2.4682391e-05
Target cost: 2.4847941e-05
Target cost: 2.501345e-05
Target cost: 2.5179766e-05
Target cost: 2.5348834e-05
Target cost: 2.5521778e-05
Target cost: 2.5695499e-05
Target cost: 2.5871144e-05
Target cost: 2.604719e-05
Target cost: 2.6223883e-05
Target cost: 2.6399734e-05
Target cost: 2.6580658e-05
Target cost: 2.6764443e-05
Target cost: 2.6949181e-05
Target cost: 2.7139573e-05
Target cost: 2.7333437e-05
Target cost: 2.7530525e-05
Target cost: 2.772871e-05
Target cost: 2.7928221e-05
Target cost: 2.8129238e-05
Target cost: 2.8334734e-05
Target cost: 2.8545857e-05
Target cost: 2.8760307e-05
Target cost: 2.8976183e-05
Target cost: 2.919479e-05
Target cost: 2.9417328e-05
Target cost: 2.9643734e-05
Target cost: 2.9872956e-05
Target cost: 3.010465e-05
Target cost: 3.0340521e-05
Target cost: 3.0580315e-05
Target cost: 3.0820658e-05
Target cost: 3.106123e-05
Target cost: 3.130557e-05
Target cost: 3.1556778e-05
Target cost: 3.181209e-05
Target cost: 3.206896e-05
Target cost: 3.233055e-05
Target cost: 3.2596156e-05
Target cost: 3.286665e-05
Target cost: 3.3141725e-05
Target cost: 3.342063e-05
Target cost: 3.370504e-05
Target cost: 3.3990644e-05
Target cost: 3.4282788e-05
Target cost: 3.458024e-05
Target cost: 3.488034e-05
Target cost: 3.5181314e-05
Target cost: 3.5481055e-05
Target cost: 3.57846e-05
Target cost: 3.609341e-05
Target cost: 3.6406283e-05
Target cost: 3.6724294e-05
Target cost: 3.7051337e-05
Target cost: 3.7386253e-05
Target cost: 3.7724923e-05
Target cost: 3.8072547e-05
Target cost: 3.842633e-05
Target cost: 3.8783626e-05
Target cost: 3.9150058e-05
Target cost: 3.9520044e-05
Target cost: 3.989047e-05
Target cost: 4.0266044e-05
Target cost: 4.0649404e-05
Target cost: 4.1035928e-05
Target cost: 4.142724e-05
Target cost: 4.1829484e-05
Target cost: 4.224565e-05
Target cost: 4.2673804e-05
Target cost: 4.310886e-05
Target cost: 4.3556534e-05
Target cost: 4.4010256e-05
Target cost: 4.447474e-05
Target cost: 4.4942975e-05
Target cost: 4.5418245e-05
Target cost: 4.5908204e-05
Target cost: 4.6415582e-05
Target cost: 4.692269e-05
Target cost: 4.7430167e-05
Target cost: 4.7938e-05
Target cost: 4.84504e-05
Target cost: 4.8968006e-05
Target cost: 4.949242e-05
Target cost: 5.0027385e-05
Target cost: 5.0580216e-05
Target cost: 5.113591e-05
Target cost: 5.169555e-05
Target cost: 5.2257878e-05
Target cost: 5.2830805e-05
Target cost: 5.3419684e-05
Target cost: 5.4015105e-05
Target cost: 5.461661e-05
Target cost: 5.5228946e-05
Target cost: 5.5854107e-05
Target cost: 5.6493194e-05
Target cost: 5.7148693e-05
Target cost: 5.7815592e-05
Target cost: 5.849636e-05
Target cost: 5.9183323e-05
Target cost: 5.9886475e-05
Target cost: 6.061715e-05
Target cost: 6.136193e-05
Target cost: 6.2105784e-05
Target cost: 6.2858344e-05
Target cost: 6.362709e-05
Target cost: 6.441734e-05
Target cost: 6.522279e-05
Target cost: 6.6040695e-05
Target cost: 6.687356e-05
Target cost: 6.772047e-05
Target cost: 6.858291e-05
Target cost: 6.946925e-05
Target cost: 7.035742e-05
Target cost: 7.126572e-05
Target cost: 7.218893e-05
Target cost: 7.312763e-05
Target cost: 7.408886e-05
Target cost: 7.504313e-05
Target cost: 7.598655e-05
Target cost: 7.695099e-05
Target cost: 7.791552e-05
Target cost: 7.888022e-05
Target cost: 7.986943e-05
Target cost: 8.0875776e-05
Target cost: 8.189471e-05
Target cost: 8.291507e-05
Target cost: 8.3945066e-05
Target cost: 8.500379e-05
Target cost: 8.6104184e-05
Target cost: 8.723955e-05
Target cost: 8.839777e-05
Target cost: 8.959746e-05
Target cost: 9.08034e-05
Target cost: 9.201776e-05
Target cost: 9.3258335e-05
Target cost: 9.451454e-05
Target cost: 9.576789e-05
Target cost: 9.7040196e-05
Target cost: 9.833352e-05
Target cost: 9.964974e-05
Target cost: 0.0001009808
Target cost: 0.00010232281
Target cost: 0.00010369518
Target cost: 0.00010510001
Target cost: 0.0001065355
Target cost: 0.00010800072
Target cost: 0.00010948222
Target cost: 0.000110978006
Target cost: 0.00011248427
Target cost: 0.000113994596
Target cost: 0.00011553715
Target cost: 0.00011712434
Target cost: 0.000118756856
Target cost: 0.00012043856
Target cost: 0.00012215332
Target cost: 0.00012387638
Target cost: 0.00012562788
Target cost: 0.00012739823
Target cost: 0.00012918722
Target cost: 0.00013101092
Target cost: 0.00013288077
Target cost: 0.00013476305
Target cost: 0.00013664828
Target cost: 0.0001385942
Target cost: 0.00014057751
Target cost: 0.00014256216
Target cost: 0.00014457243
Target cost: 0.000146584
Target cost: 0.00014863606
Target cost: 0.00015072545
Target cost: 0.00015287455
Target cost: 0.00015508162
Target cost: 0.00015736918
Target cost: 0.00015970695
Target cost: 0.00016210407
Target cost: 0.00016451335
Target cost: 0.00016695366
Target cost: 0.00016943243
Target cost: 0.00017195381
Target cost: 0.00017455441
Target cost: 0.00017725285
Target cost: 0.00018003606
Target cost: 0.00018284639
Target cost: 0.00018569798
Target cost: 0.00018867129
Target cost: 0.00019180318
Target cost: 0.00019503155
Target cost: 0.00019835655
Target cost: 0.00020175986
Target cost: 0.00020530542
Target cost: 0.00020891847
Target cost: 0.00021258186
Target cost: 0.00021639619
Target cost: 0.0002203383
Target cost: 0.0002244212
Target cost: 0.00022859286
Target cost: 0.00023286398
Target cost: 0.00023724698
Target cost: 0.00024173934
Target cost: 0.00024643255
Target cost: 0.00025125282
Target cost: 0.00025616976
Target cost: 0.00026129137
Target cost: 0.000266631
Target cost: 0.0002723784
Target cost: 0.0002784072
Target cost: 0.00028467542
Target cost: 0.00029110457
Target cost: 0.00029780253
Target cost: 0.00030476347
Target cost: 0.0003120721
Target cost: 0.00031983832
Target cost: 0.00032798096
Target cost: 0.00033635806
Target cost: 0.00034503647
Target cost: 0.00035411908
Target cost: 0.0003637158
Target cost: 0.00037383786
Target cost: 0.00038453852
Target cost: 0.00039584303
Target cost: 0.00040743948
Target cost: 0.0004194209
Target cost: 0.0004321291
Target cost: 0.0004457811
Target cost: 0.00046025508
Target cost: 0.00047543613
Target cost: 0.0004915014
Target cost: 0.00050889805
Target cost: 0.0005277905
Target cost: 0.0005477197
Target cost: 0.0005689843
Target cost: 0.0005919691
Target cost: 0.0006164645
Target cost: 0.0006431704
Target cost: 0.00067365856
Target cost: 0.00070696115
Target cost: 0.0007441341
Target cost: 0.0007859295
Target cost: 0.0008325074
Target cost: 0.0008860525
Target cost: 0.0009491721
Target cost: 0.0010239397
Target cost: 0.0011059969
Target cost: 0.0011970086
Target cost: 0.0012998465
Target cost: 0.0014156094
Target cost: 0.0015515444
Target cost: 0.0017023605
Target cost: 0.0018585636
Target cost: 0.0020683652
Target cost: 0.002308786
Target cost: 0.0026234584
Target cost: 0.002945915
Target cost: 0.0033946913
Target cost: 0.0039548175
Target cost: 0.0045176772
Target cost: 0.005285658
Target cost: 0.005951518
Target cost: 0.007335698
Target cost: 0.009411078
Target cost: 0.009198565
Target cost: 0.022070294
Target cost: 0.006540424
Target cost: 0.043852877
Target cost: 0.030337676
Target cost: 0.1400359
Target cost: 0.062367123
Target cost: 0.62258416
Target cost: 0.005081448
Target cost: 0.026586896
Target cost: 0.616901
Target cost: 0.29153734
Target cost: 0.83902586
Target cost: 0.9756715
CPU times: user 2min 39s, sys: 6.19 s, total: 2min 45s
Wall time: 27.7 s
In [21]:
adv_img2 = np.copy(adv)
adv_img2 = adv_img2[0]
adv_img2 /= 2
adv_img2 += 0.5
adv_img2 *= 255
adv_img2 = adv_img2.astype(np.uint8)
fig1 = plt.figure(figsize=(15,15))
ax1 = fig1.add_subplot(1,2,1)
ax1.imshow(x_img)
ax1.set_title("original")
ax1 = fig1.add_subplot(1,2,2)
ax1.imshow(adv_img2)
ax1.set_title("resultado")
plt.show()
decode_predictions(iv3.predict(adv))
Out[21]:
[[('n02123394', 'Persian_cat', 0.99657923),
('n02328150', 'Angora', 0.0011756226),
('n02111889', 'Samoyed', 0.0006940528),
('n02111500', 'Great_Pyrenees', 0.0004527076),
('n02104029', 'kuvasz', 0.00022161647)]]
In [ ]: