我正在try 在tensorflow中为一个图像分类任务实现t-SNE可视化.我在网上主要发现的都是在Pytorch中实现的.见here.
以下是我的通用代码，用于培训目的，效果非常好，只想将t-SNE可视化添加到其中:
import pandas as pd
import numpy as np
import tensorflow as tf
import cv2
from tensorflow import keras
from tensorflow.keras import layers, Input
from tensorflow.keras.layers import Dense, InputLayer, Flatten
from tensorflow.keras.models import Sequential, Model
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
from PIL import Image
from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
.
.
.
base_model=tf.keras.applications.ResNet152(
include_top=False, weights='imagenet', input_tensor=None,
input_shape=None, pooling=None)
.
.
.
base_model.trainable = False
# Create new model on top.
inputs = tf.keras.Input(shape=(IMG_WIDTH, IMG_HEIGHT, 3))
x = base_model(inputs, training=False)
x=keras.layers.Flatten()(x)
x = keras.layers.Dense(64)(x)
x=layers.Activation('relu')(x)
x=keras.layers.Flatten()(x)
x = keras.layers.Dense(32)(x)
x=layers.Activation('relu')(x)
x = keras.layers.Dense(2)(x)
outputs=layers.Activation('softmax')(x)
model=keras.Model(inputs, outputs)
vaidation_datagen = ImageDataGenerator(rotation_range=90,
zoom_range=0.2,
horizontal_flip=True,
vertical_flip=True)
train_generator = train_datagen.flow_from_directory(
train_path, # this is the target directory
target_size=target_size, # all images will be resized to the target size
color_mode='rgb',
batch_size=batch_size,
shuffle=True,
class_mode='categorical',
interpolation='nearest',
seed=42) # since we use binary_crossentropy loss, we need binary labels
validation_generator = vaidation_datagen.flow_from_directory(
validation_path, # this is the target directory
target_size=target_size, # all images will be resized to the target size
color_mode='rgb',
batch_size=batch_size,
shuffle=True,
class_mode='categorical',
interpolation='nearest',
seed=42)
model.compile(optimizer, loss , metrics)
model_checkpoint = tf.keras.callbacks.ModelCheckpoint((model_path+model_filename), monitor='val_loss',verbose=1, save_best_only=True)
model.summary()
history = model.fit(
train_generator,
steps_per_epoch = num_of_train_img_raw//batch_size,
epochs = epochs,
validation_data = validation_generator, # relates to the validation data.
validation_steps = num_of_val_img_raw//batch_size,
callbacks=[model_checkpoint],
use_multiprocessing = False)
根据提供的参考链接，我似乎需要首先保存特征，然后按照如下方式应用t-SNE(该部分从here复制并粘贴):
tsne = TSNE(n_components=2).fit_transform(features)
# scale and move the coordinates so they fit [0; 1] range
def scale_to_01_range(x):
# compute the distribution range
value_range = (np.max(x) - np.min(x))
# move the distribution so that it starts from zero
# by extracting the minimal value from all its values
starts_from_zero = x - np.min(x)
# make the distribution fit [0; 1] by dividing by its range
return starts_from_zero / value_range
# extract x and y coordinates representing the positions of the images on T-SNE plot
tx = tsne[:, 0]
ty = tsne[:, 1]
tx = scale_to_01_range(tx)
ty = scale_to_01_range(ty)
# initialize a matplotlib plot
fig = plt.figure()
ax = fig.add_subplot(111)
# for every class, we'll add a scatter plot separately
for label in colors_per_class:
# find the samples of the current class in the data
indices = [i for i, l in enumerate(labels) if l == label]
# extract the coordinates of the points of this class only
current_tx = np.take(tx, indices)
current_ty = np.take(ty, indices)
# convert the class color to matplotlib format
color = np.array(colors_per_class[label], dtype=np.float) / 255
# add a scatter plot with the corresponding color and label
ax.scatter(current_tx, current_ty, c=color, label=label)
# build a legend using the labels we set previously
ax.legend(loc='best')
# finally, show the plot
plt.show()
如果你能帮我把这两件事联系起来，我将不胜感激.
