Description
Please go to TF Forum for help and support:
https://discuss.tensorflow.org/tag/keras
If you open a GitHub issue, here is our policy:
It must be a bug, a feature request, or a significant problem with the documentation (for small docs fixes please send a PR instead).
The form below must be filled out.
Here's why we have that policy:.
Keras developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.
System information.
- Have I written custom code (as opposed to using a stock example script provided in Keras):Yes
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): ubuntu 20.04 LST
- TensorFlow installed from (source or binary): binary
- TensorFlow version (use command below): 2.11.0
- Python version: 3.9.0
- Bazel version (if compiling from source):
- GPU model and memory: RTX 3090, 24GB memory.
- Exact command to reproduce:
I have used functional API to build a ResNet neural network algorithm. However, this constructed model can not save the neural networks' weights in an appropriate result. The validated result was very good. But the inference result is very bad.
When I use tf.config.run_functions_eagerly(True) in the model training stage, the inference result is very good. Otherwise, the inference result was very bad. To tackle this problem, I have searched some sample code of Keras.applications. Bud, not working in model weight save.
The implemented ResNet code is shown in follow:
Describe the problem.
import tensorflow.compat.v2 as tf
from keras.regularizers import l2
from keras import layers
from keras.engine import sequential
from keras.engine import training as training_lib
import keras as K
identitys=None
def Bottleneck(inputs, out_channel, name, downsample, strides=1):
expansion = 4
key = out_channel * expansion
identity = inputs
global identitys
if downsample:
identitys = layers.Conv2D(key, kernel_size=1, strides=strides,
use_bias=False, kernel_initializer='he_normal',
padding="SAME", kernel_regularizer=l2(1.e-5), name=name + "ds_conv")(identity)
identitys = layers.BatchNormalization(momentum=0.9, epsilon=1e-5, name=name + "ds_normal")(identitys)
else:
identitys = inputs
xb = layers.Conv2D(out_channel, kernel_size=1, use_bias=False, kernel_initializer='he_normal',
kernel_regularizer=l2(1.e-4), name=name + "Conv2D_1")(inputs)
xb = layers.BatchNormalization(momentum=0.9,
epsilon=1e-5, name=name + "BN_1")(xb)
xb = layers.Activation(tf.keras.activations.swish, name=name + "ACT_1")(xb)
xb = layers.Conv2D(out_channel, kernel_size=3, use_bias=False, strides=strides, padding="SAME",
kernel_initializer='he_normal', kernel_regularizer=l2(1.e-4), name=name + "Conv2D_2")(xb)
xb = layers.BatchNormalization(momentum=0.9,
epsilon=1e-5, name=name + "BN_3")(xb)
xb = layers.ReLU(name=name + "ReLU")(xb)
xb = layers.Conv2D(key, kernel_size=1, use_bias=False,
kernel_initializer='he_normal',
kernel_regularizer=l2(1.e-4),
name=name + "Conv2D_3")(xb)
xb = layers.BatchNormalization(momentum=0.9,
epsilon=1e-5,
name=name + "BN_4")(xb)
xb = layers.Add(name=name + "addition")([identitys, xb])
xb = layers.BatchNormalization(momentum=0.9,
epsilon=1e-5,
name=name + "Last_BN")(xb)
xb = layers.ReLU(name=name + "LastReLU")(xb)
return xb
def _make_layer(inputs, make_block, channel, block_num, layer_name, strides, down_sample):
i = 0
name = layer_name + f"block_{i + 1}_"
xm = make_block(inputs=inputs, out_channel=channel, name=name, strides=strides, downsample=down_sample)
for i in range(1, block_num):
i += 1
name = layer_name + f"block_{i}_"
xm = make_block(inputs=xm, out_channel=channel, name=name, strides=1, downsample=False)
return xm
class ResnetBuilder(object):
@staticmethod
def build(block, blocks_num, im_width=224, im_height=224, num_classes=1000):
img_input = layers.Input(shape=(im_width, im_height, 3),
dtype="float32",
name="layers_inputs")
x = layers.Conv2D(filters=64, kernel_size=7, strides=2,
padding="SAME", use_bias=False,
name="layers_conv1")(img_input) # 把这一行替换成ContourOperator
x = layers.BatchNormalization(momentum=0.9, epsilon=1e-5, name="x_FBN")(x)
x = layers.ReLU(name="FReLU")(x)
x = layers.DepthwiseConv2D(kernel_size=3, padding="SAME", use_bias=False,
depthwise_initializer=tf.keras.initializers.TruncatedNormal(mean=0.0,
stddev=0.05, seed=None)
, kernel_regularizer=l2(1.e-4), name="FDW")(x)
x = layers.BatchNormalization(momentum=0.9, epsilon=1e-5, name="FBN")(x)
x = layers.Activation(tf.keras.activations.swish, name="FirstACT")(x)
x = layers.MaxPool2D(pool_size=3, strides=2, padding="SAME", name="f_MP")(x)
x = _make_layer(x, block, 64, block_num=blocks_num[0], layer_name="ml1", strides=1, down_sample=True)
x = _make_layer(x, block, 128, block_num=blocks_num[1], layer_name="ml2", strides=2, down_sample=True)
x = _make_layer(x, block, 256, block_num=blocks_num[2], layer_name="ml3", strides=2, down_sample=True)
x = _make_layer(x, block, 512, block_num=blocks_num[3], layer_name="ml4", strides=2, down_sample=True)
x = layers.GlobalAvgPool2D(name="GAP2D")(x) # pool + flatten
x = layers.Dense(num_classes, name="logits")(x)
predict = layers.Softmax(name="SoftMax")(x)
model = training_lib.Model(inputs=img_input,
outputs=predict, name="model")
return model
@staticmethod
def resnet101(im_width=448, im_height=448,
include_top=True, num_classes=5):
return ResnetBuilder.build(Bottleneck, [3, 4, 23, 3],
im_width, im_height, num_classes)
@staticmethod
def resnet50(im_width=448, im_height=448,
include_top=True,
num_classes=5, **kwargs):
return ResnetBuilder.build(Bottleneck, [3, 4, 6, 3],
im_width, im_height, num_classes)
Describe the problem clearly here. Be sure to convey here why it's a bug in Keras or why the requested feature is needed.
Describe the current behavior.
The inference result can not match the validation result.
The inference code is shown in follow:
from keras import Model
from keras.utils import image_utils
import tensorflow as tf
import numpy as np
import os
from test_code import ResnetBuilder
gpus = tf.config.experimental.list_physical_devices(device_type='GPU')
tf.config.experimental.set_visible_devices(devices=gpus[1], device_type='GPU')
def preprocess_image(img_path, target_size=(448, 448)):
"""Preprocess the image by reshape and normalization.
Args:
img_path: A string.
target_size: A tuple, reshape to this size.
Return:
An image ndarray.
"""
img = image_utils.load_img(img_path, target_size=target_size)
img = image_utils.img_to_array(img)
img /= 255.0
return img
def load_trained_model():
model = ResnetBuilder.resnet50(448, 448, 5)
model_name = r"./model.30-.h5"
model.load_weights(model_name, by_name=True)
print('model load success.')
return model
def get_category_name(full_image_path, model):
img = preprocess_image(full_image_path)
img_tensor = np.expand_dims(img, axis=0)
heatmap_model = Model([model.inputs], [model.output])
predictions = heatmap_model(img_tensor)
category_id = np.argmax(predictions[0])
label_name = ['A1', 'A2', 'A3', "A4", "A5"]
category_name = label_name[category_id]
return category_name
model = load_trained_model()
model.summary()
image_folder = r"[Image_Path]"
save_path = r"[Save_Path]"
name_list = os.listdir(image_folder)
for file_name in name_list:
full_image_name = image_folder + "/" + file_name
category_name = get_category_name(full_image_name, model)
save_name = category_name + "_" + file_name # just print result, not save image with a new name.
print(save_name)
Describe the expected behavior.
Saving the right weights in model.h5 files, and inference the right result from the weight model.
Standalone code to reproduce the issue.
Provide a reproducible test case that is the bare minimum necessary to generate
the problem. If possible, please share a link to Colab/Jupyter/any notebook.
Sorry, inconvenient to provide