为什么加载模型时 Tensorflow 多类图像预测不起作用？
Why does Tensorflow multiclass-image-prediction not work when model is loaded?
我目前正在尝试学习机器学习技术，并想用 tensorflow 重新创建一个简单的图像识别算法。因此，我制作了两个 Python 文件：一个用于训练，一个用于预测。
测试于 Ubuntu 18.04
使用 Python 版本：3.7
使用的 Numpy 版本：1.18.1
使用的 Tensorflow 版本：1.14 和 2.1.0（以下输出来自版本 1.14）
我的图片来自http://www.cs.columbia.edu/CAVE/databases/pubfig/download/#dev
该集包含来自 60 个人的大约 3000 张裁剪面孔图像。
train_model.py:
#!/usr/bin/env python
import concurrent.futures
import pandas as pd
import urllib
import pathlib
import hashlib
import os
import json
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
people = pd.read_csv("dev_people.txt")
image_generator = ImageDataGenerator(rescale=1./255, validation_split=0.2, rotation_range=45, zoom_range=0.2)
IMG_HEIGHT = 128
IMG_WIDTH = 128
LEARNING_RATE = 0.0001
BATCH_SIZE = 32
NUM_TRAIN = 100
STEPS_PER_EPOCH = round(NUM_TRAIN) // BATCH_SIZE
VAL_STEPS = 20
NUM_EPOCHS = 3
train_data = image_generator.flow_from_directory(batch_size=BATCH_SIZE,
directory="persons-cropped",
shuffle=True,
target_size=(IMG_HEIGHT, IMG_WIDTH),
class_mode="categorical",
subset="training")
labels = train_data.class_indices
labels = {v: k for k, v in labels.items()}
with open("labels.json", "w") as labels_file:
labels_file.write(json.dumps(labels))
validation_data = image_generator.flow_from_directory(batch_size=BATCH_SIZE,
directory="persons-cropped",
shuffle=True,
target_size=(IMG_HEIGHT, IMG_WIDTH),
class_mode="categorical",
subset="validation")
base_model = tf.keras.applications.MobileNetV2(
input_shape=(IMG_WIDTH, IMG_HEIGHT, 3),
include_top=False,
weights="imagenet"
)
base_model.trainable = False
maxpool_layer = tf.keras.layers.GlobalMaxPooling2D()
prediction_layer = tf.keras.layers.Dense(60, activation="sigmoid")
dropout_layer = tf.keras.layers.Dropout(0.2)
model = tf.keras.Sequential([
base_model,
maxpool_layer,
# dropout_layer,
prediction_layer
])
model.compile(optimizer=tf.keras.optimizers.Adam(lr=LEARNING_RATE),
loss="binary_crossentropy",
metrics=["accuracy"]
)
model.summary()
model.fit(
train_data,
epochs=NUM_EPOCHS,
steps_per_epoch=None,
validation_data=validation_data,
validation_steps=None,
use_multiprocessing=False,
workers=6,
verbose=2
)
model.save("model.h5")
输出：
Found 2431 images belonging to 60 classes.
Found 573 images belonging to 60 classes.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
2020-01-25 22:23:40.036326: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2599985000 Hz
2020-01-25 22:23:40.036657: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x6b81c60 executing computations on platform Host. Devices:
2020-01-25 22:23:40.036789: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): <undefined>, <undefined>
2020-01-25 22:23:40.615771: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set. If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU. To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
mobilenetv2_1.00_224 (Model) (None, 7, 7, 1280) 2257984
_________________________________________________________________
global_max_pooling2d (Global (None, 1280) 0
_________________________________________________________________
dense (Dense) (None, 60) 76860
=================================================================
Total params: 2,334,844
Trainable params: 76,860
Non-trainable params: 2,257,984
_________________________________________________________________
Epoch 1/3
2020-01-25 22:23:55.995833: W tensorflow/core/framework/allocator.cc:107] Allocation of 154140672 exceeds 10% of system memory.
2020-01-25 22:23:56.730363: W tensorflow/core/framework/allocator.cc:107] Allocation of 156905472 exceeds 10% of system memory.
2020-01-25 22:24:02.782372: W tensorflow/core/framework/allocator.cc:107] Allocation of 154140672 exceeds 10% of system memory.
2020-01-25 22:24:03.531172: W tensorflow/core/framework/allocator.cc:107] Allocation of 156905472 exceeds 10% of system memory.
2020-01-25 22:24:09.474692: W tensorflow/core/framework/allocator.cc:107] Allocation of 154140672 exceeds 10% of system memory.
/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:788: UserWarning: Corrupt EXIF data. Expecting to read 4 bytes but only got 0.
warnings.warn(str(msg))
76/76 - 602s - loss: 0.3851 - acc: 0.9097 - val_loss: 0.1812 - val_acc: 0.9495
Epoch 2/3
76/76 - 616s - loss: 0.1480 - acc: 0.9757 - val_loss: 0.1732 - val_acc: 0.9544
Epoch 3/3
76/76 - 627s - loss: 0.1452 - acc: 0.9760 - val_loss: 0.1767 - val_acc: 0.9516
它说该模型的准确率分数约为 95%，这非常好，现在预测图像应该没有问题。但是，这是预测文件：
predict_image.py
#!/usr/bin/env python
import concurrent.futures
import pandas as pd
import numpy as np
import urllib
import pathlib
import hashlib
import os
import sys
import cv2
import json
import tensorflow as tf
import PIL
import skimage
from tensorflow.keras.preprocessing.image import ImageDataGenerator
IMG_HEIGHT = 128
IMG_WIDTH = 128
def load_image(filename):
img = tf.keras.preprocessing.image.load_img(filename, target_size=(IMG_WIDTH,IMG_HEIGHT))
img = tf.keras.preprocessing.image.img_to_array(img)
img = np.expand_dims(img, axis=0) / 255
return img
from glob import glob
class_names = glob("persons-cropped/*")
class_names = sorted(class_names)
labels_file = open("labels.json", "r")
labels = json.loads(labels_file.read())
print(labels)
model = tf.keras.models.load_model("model.h5")
model.summary()
img = load_image(sys.argv[1])
predictions = model.predict(img, verbose=1)
prediction = predictions.argmax(axis=-1)
print(predictions)
print(prediction)
map_labels = np.vectorize(lambda i: labels[str(i)])
print(map_labels(prediction))
输出：使用 Zach Braff 图像时：
{'0': 'Abhishek Bachan', '1': 'Alex Rodriguez', '2': 'Ali Landry', '3': 'Alyssa Milano', '4': 'Anderson Cooper', '5': 'Anna Paquin', '6': 'Audrey Tautou', '7': 'Barack Obama', '8': 'Ben Stiller', '9': 'Christina Ricci', '10': 'Clive Owen', '11': 'Cristiano Ronaldo', '12': 'Daniel Craig', '13': 'Danny Devito', '14': 'David Duchovny', '15': 'Denise Richards', '16': 'Diane Sawyer', '17': 'Donald Faison', '18': 'Ehud Olmert', '19': 'Faith Hill', '20': 'Famke Janssen', '21': 'Hugh Jackman', '22': 'Hugh Laurie', '23': 'James Spader', '24': 'Jared Leto', '25': 'Julia Roberts', '26': 'Julia Stiles', '27': 'Karl Rove', '28': 'Katherine Heigl', '29': 'Kevin Bacon', '30': 'Kiefer Sutherland', '31': 'Kim Basinger', '32': 'Mark Ruffalo', '33': 'Meg Ryan', '34': 'Michelle Trachtenberg', '35': 'Michelle Wie', '36': 'Mickey Rourke', '37': 'Miley Cyrus', '38': 'Milla Jovovich', '39': 'Nicole Richie', '40': 'Rachael Ray', '41': 'Robert Gates', '42': 'Ryan Seacrest', '43': 'Sania Mirza', '44': 'Sarah Chalke', '45': 'Sarah Palin', '46': 'Scarlett Johansson', '47': 'Seth Rogen', '48': 'Shahrukh Khan', '49': 'Shakira', '50': 'Stephen Colbert', '51': 'Stephen Fry', '52': 'Steve Carell', '53': 'Steve Martin', '54': 'Tracy Morgan', '55': 'Ty Pennington', '56': 'Viggo Mortensen', '57': 'Wilmer Valderrama', '58': 'Zac Efron', '59': 'Zach Braff'}
2020-01-25 22:58:05.582049: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2599985000 Hz
2020-01-25 22:58:05.582514: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x618f910 executing computations on platform Host. Devices:
2020-01-25 22:58:05.582653: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): <undefined>, <undefined>
2020-01-25 22:58:06.454565: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set. If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU. To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.
1/1 [==============================] - 1s 999ms/sample
[[3.23683023e-04 6.47217035e-04 3.90201807e-04 2.69789696e-02
2.17908323e-02 1.53781831e-01 4.79090214e-03 8.64863396e-04
1.11432403e-01 8.87395382e-01 3.30170989e-03 2.17252970e-03
1.78458661e-01 1.09243691e-02 1.47551298e-04 2.62927115e-02
3.22049320e-01 2.69562006e-04 9.11523938e-01 2.44581699e-03
7.65213370e-03 2.90286541e-03 1.01376325e-01 6.43432140e-05
4.43832874e-02 3.94093990e-03 6.90050423e-02 7.47233629e-04
1.05589628e-03 8.04662704e-07 3.76045704e-03 4.28827941e-01
1.20029151e-02 1.77664489e-01 5.27173281e-04 2.45797634e-03
5.89579344e-03 9.46103930e-01 2.79089808e-03 2.09265649e-02
2.83238888e-02 4.86207008e-03 8.15459788e-02 1.30202770e-02
1.50602162e-02 1.33922696e-03 1.24056339e-02 5.76970875e-02
2.65627503e-02 5.18084109e-01 4.89562750e-04 3.15269828e-03
4.88847494e-04 2.13665128e-01 1.40489936e-02 2.93705761e-02
5.01989722e-02 1.21492555e-03 1.62564263e-01 2.91267484e-01]]
[37]
['Miley Cyrus']
预测算法一直都是错误的。如果我使用其他 Zach Braff 图像，对于同一张图片，输出当然保持不变，但在 10 个测试用例中，它从来不是 Zach Braff，而是始终是不同的人。 （不仅是麦莉·赛勒斯，还有夏奇拉、史蒂夫·卡瑞尔……）
我在此处用作输入的任何 class 都会出现此模式。
我没有在互联网上找到任何有用的建议，并尝试了我能想到的每个参数组合都可以工作。我还使用了两个版本的 Tensorflow 并确保所有库都是最新的。
嘿，我相信你得到了奇怪的预测，因为你的数据分布有 60 类 人，而你的模型是使用设置为二元交叉熵的损失函数编译的。
二元交叉熵用于确定最大值 2 类。您需要做的是将损失函数更改为分类交叉熵。
model.compile(optimizer=tf.keras.optimizers.Adam(lr=LEARNING_RATE),
loss="categorical_crossentropy", # here
metrics=["accuracy"]
)
我目前正在尝试学习机器学习技术，并想用 tensorflow 重新创建一个简单的图像识别算法。因此，我制作了两个 Python 文件：一个用于训练，一个用于预测。
测试于 Ubuntu 18.04 使用 Python 版本：3.7 使用的 Numpy 版本：1.18.1 使用的 Tensorflow 版本：1.14 和 2.1.0（以下输出来自版本 1.14）
我的图片来自http://www.cs.columbia.edu/CAVE/databases/pubfig/download/#dev 该集包含来自 60 个人的大约 3000 张裁剪面孔图像。
train_model.py:
#!/usr/bin/env python
import concurrent.futures
import pandas as pd
import urllib
import pathlib
import hashlib
import os
import json
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
people = pd.read_csv("dev_people.txt")
image_generator = ImageDataGenerator(rescale=1./255, validation_split=0.2, rotation_range=45, zoom_range=0.2)
IMG_HEIGHT = 128
IMG_WIDTH = 128
LEARNING_RATE = 0.0001
BATCH_SIZE = 32
NUM_TRAIN = 100
STEPS_PER_EPOCH = round(NUM_TRAIN) // BATCH_SIZE
VAL_STEPS = 20
NUM_EPOCHS = 3
train_data = image_generator.flow_from_directory(batch_size=BATCH_SIZE,
directory="persons-cropped",
shuffle=True,
target_size=(IMG_HEIGHT, IMG_WIDTH),
class_mode="categorical",
subset="training")
labels = train_data.class_indices
labels = {v: k for k, v in labels.items()}
with open("labels.json", "w") as labels_file:
labels_file.write(json.dumps(labels))
validation_data = image_generator.flow_from_directory(batch_size=BATCH_SIZE,
directory="persons-cropped",
shuffle=True,
target_size=(IMG_HEIGHT, IMG_WIDTH),
class_mode="categorical",
subset="validation")
base_model = tf.keras.applications.MobileNetV2(
input_shape=(IMG_WIDTH, IMG_HEIGHT, 3),
include_top=False,
weights="imagenet"
)
base_model.trainable = False
maxpool_layer = tf.keras.layers.GlobalMaxPooling2D()
prediction_layer = tf.keras.layers.Dense(60, activation="sigmoid")
dropout_layer = tf.keras.layers.Dropout(0.2)
model = tf.keras.Sequential([
base_model,
maxpool_layer,
# dropout_layer,
prediction_layer
])
model.compile(optimizer=tf.keras.optimizers.Adam(lr=LEARNING_RATE),
loss="binary_crossentropy",
metrics=["accuracy"]
)
model.summary()
model.fit(
train_data,
epochs=NUM_EPOCHS,
steps_per_epoch=None,
validation_data=validation_data,
validation_steps=None,
use_multiprocessing=False,
workers=6,
verbose=2
)
model.save("model.h5")
输出：
Found 2431 images belonging to 60 classes.
Found 573 images belonging to 60 classes.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
2020-01-25 22:23:40.036326: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2599985000 Hz
2020-01-25 22:23:40.036657: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x6b81c60 executing computations on platform Host. Devices:
2020-01-25 22:23:40.036789: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): <undefined>, <undefined>
2020-01-25 22:23:40.615771: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set. If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU. To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
mobilenetv2_1.00_224 (Model) (None, 7, 7, 1280) 2257984
_________________________________________________________________
global_max_pooling2d (Global (None, 1280) 0
_________________________________________________________________
dense (Dense) (None, 60) 76860
=================================================================
Total params: 2,334,844
Trainable params: 76,860
Non-trainable params: 2,257,984
_________________________________________________________________
Epoch 1/3
2020-01-25 22:23:55.995833: W tensorflow/core/framework/allocator.cc:107] Allocation of 154140672 exceeds 10% of system memory.
2020-01-25 22:23:56.730363: W tensorflow/core/framework/allocator.cc:107] Allocation of 156905472 exceeds 10% of system memory.
2020-01-25 22:24:02.782372: W tensorflow/core/framework/allocator.cc:107] Allocation of 154140672 exceeds 10% of system memory.
2020-01-25 22:24:03.531172: W tensorflow/core/framework/allocator.cc:107] Allocation of 156905472 exceeds 10% of system memory.
2020-01-25 22:24:09.474692: W tensorflow/core/framework/allocator.cc:107] Allocation of 154140672 exceeds 10% of system memory.
/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:788: UserWarning: Corrupt EXIF data. Expecting to read 4 bytes but only got 0.
warnings.warn(str(msg))
76/76 - 602s - loss: 0.3851 - acc: 0.9097 - val_loss: 0.1812 - val_acc: 0.9495
Epoch 2/3
76/76 - 616s - loss: 0.1480 - acc: 0.9757 - val_loss: 0.1732 - val_acc: 0.9544
Epoch 3/3
76/76 - 627s - loss: 0.1452 - acc: 0.9760 - val_loss: 0.1767 - val_acc: 0.9516
它说该模型的准确率分数约为 95%，这非常好，现在预测图像应该没有问题。但是，这是预测文件：
predict_image.py
#!/usr/bin/env python
import concurrent.futures
import pandas as pd
import numpy as np
import urllib
import pathlib
import hashlib
import os
import sys
import cv2
import json
import tensorflow as tf
import PIL
import skimage
from tensorflow.keras.preprocessing.image import ImageDataGenerator
IMG_HEIGHT = 128
IMG_WIDTH = 128
def load_image(filename):
img = tf.keras.preprocessing.image.load_img(filename, target_size=(IMG_WIDTH,IMG_HEIGHT))
img = tf.keras.preprocessing.image.img_to_array(img)
img = np.expand_dims(img, axis=0) / 255
return img
from glob import glob
class_names = glob("persons-cropped/*")
class_names = sorted(class_names)
labels_file = open("labels.json", "r")
labels = json.loads(labels_file.read())
print(labels)
model = tf.keras.models.load_model("model.h5")
model.summary()
img = load_image(sys.argv[1])
predictions = model.predict(img, verbose=1)
prediction = predictions.argmax(axis=-1)
print(predictions)
print(prediction)
map_labels = np.vectorize(lambda i: labels[str(i)])
print(map_labels(prediction))
输出：使用 Zach Braff 图像时：
{'0': 'Abhishek Bachan', '1': 'Alex Rodriguez', '2': 'Ali Landry', '3': 'Alyssa Milano', '4': 'Anderson Cooper', '5': 'Anna Paquin', '6': 'Audrey Tautou', '7': 'Barack Obama', '8': 'Ben Stiller', '9': 'Christina Ricci', '10': 'Clive Owen', '11': 'Cristiano Ronaldo', '12': 'Daniel Craig', '13': 'Danny Devito', '14': 'David Duchovny', '15': 'Denise Richards', '16': 'Diane Sawyer', '17': 'Donald Faison', '18': 'Ehud Olmert', '19': 'Faith Hill', '20': 'Famke Janssen', '21': 'Hugh Jackman', '22': 'Hugh Laurie', '23': 'James Spader', '24': 'Jared Leto', '25': 'Julia Roberts', '26': 'Julia Stiles', '27': 'Karl Rove', '28': 'Katherine Heigl', '29': 'Kevin Bacon', '30': 'Kiefer Sutherland', '31': 'Kim Basinger', '32': 'Mark Ruffalo', '33': 'Meg Ryan', '34': 'Michelle Trachtenberg', '35': 'Michelle Wie', '36': 'Mickey Rourke', '37': 'Miley Cyrus', '38': 'Milla Jovovich', '39': 'Nicole Richie', '40': 'Rachael Ray', '41': 'Robert Gates', '42': 'Ryan Seacrest', '43': 'Sania Mirza', '44': 'Sarah Chalke', '45': 'Sarah Palin', '46': 'Scarlett Johansson', '47': 'Seth Rogen', '48': 'Shahrukh Khan', '49': 'Shakira', '50': 'Stephen Colbert', '51': 'Stephen Fry', '52': 'Steve Carell', '53': 'Steve Martin', '54': 'Tracy Morgan', '55': 'Ty Pennington', '56': 'Viggo Mortensen', '57': 'Wilmer Valderrama', '58': 'Zac Efron', '59': 'Zach Braff'}
2020-01-25 22:58:05.582049: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2599985000 Hz
2020-01-25 22:58:05.582514: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x618f910 executing computations on platform Host. Devices:
2020-01-25 22:58:05.582653: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): <undefined>, <undefined>
2020-01-25 22:58:06.454565: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set. If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU. To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.
1/1 [==============================] - 1s 999ms/sample
[[3.23683023e-04 6.47217035e-04 3.90201807e-04 2.69789696e-02
2.17908323e-02 1.53781831e-01 4.79090214e-03 8.64863396e-04
1.11432403e-01 8.87395382e-01 3.30170989e-03 2.17252970e-03
1.78458661e-01 1.09243691e-02 1.47551298e-04 2.62927115e-02
3.22049320e-01 2.69562006e-04 9.11523938e-01 2.44581699e-03
7.65213370e-03 2.90286541e-03 1.01376325e-01 6.43432140e-05
4.43832874e-02 3.94093990e-03 6.90050423e-02 7.47233629e-04
1.05589628e-03 8.04662704e-07 3.76045704e-03 4.28827941e-01
1.20029151e-02 1.77664489e-01 5.27173281e-04 2.45797634e-03
5.89579344e-03 9.46103930e-01 2.79089808e-03 2.09265649e-02
2.83238888e-02 4.86207008e-03 8.15459788e-02 1.30202770e-02
1.50602162e-02 1.33922696e-03 1.24056339e-02 5.76970875e-02
2.65627503e-02 5.18084109e-01 4.89562750e-04 3.15269828e-03
4.88847494e-04 2.13665128e-01 1.40489936e-02 2.93705761e-02
5.01989722e-02 1.21492555e-03 1.62564263e-01 2.91267484e-01]]
[37]
['Miley Cyrus']
预测算法一直都是错误的。如果我使用其他 Zach Braff 图像，对于同一张图片，输出当然保持不变，但在 10 个测试用例中，它从来不是 Zach Braff，而是始终是不同的人。 （不仅是麦莉·赛勒斯，还有夏奇拉、史蒂夫·卡瑞尔……）
我在此处用作输入的任何 class 都会出现此模式。
我没有在互联网上找到任何有用的建议，并尝试了我能想到的每个参数组合都可以工作。我还使用了两个版本的 Tensorflow 并确保所有库都是最新的。
嘿，我相信你得到了奇怪的预测，因为你的数据分布有 60 类 人，而你的模型是使用设置为二元交叉熵的损失函数编译的。
二元交叉熵用于确定最大值 2 类。您需要做的是将损失函数更改为分类交叉熵。
model.compile(optimizer=tf.keras.optimizers.Adam(lr=LEARNING_RATE),
loss="categorical_crossentropy", # here
metrics=["accuracy"]
)