如何将图像数据正确地拟合到 python 中的模型？
How to fit image data correctly to a model in python?
我正在尝试训练一个 cnn 模型，但我真的不知道如何正确地训练它。我还在学习这类东西，所以我真的迷路了。我已经尝试用它做一些事情，但仍然无法理解它。有人可以向我解释如何正确地做到这一点。当我尝试将训练数据拟合到模型时，会弹出此错误。
WARNING:tensorflow:Model was constructed with shape (None, 224, 224, 3) for input KerasTensor(type_spec=TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1'), name='input_1', description="created by layer 'input_1'"), but it was called on an input with incompatible shape (None,).
Traceback (most recent call last):
File "G:/Skripsi/Program/training.py", line 80, in <module>
train.train()
File "G:/Skripsi/Program/training.py", line 70, in train
model.fit(self.x_train, self.y_train, epochs=2, verbose=1)
File "G:\Skripsi\Program\venv\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "G:\Skripsi\Program\venv\lib\site-packages\tensorflow\python\framework\func_graph.py", line 1129, in autograph_handler
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
File "G:\Skripsi\Program\venv\lib\site-packages\keras\engine\training.py", line 878, in train_function *
return step_function(self, iterator)
File "G:\Skripsi\Program\venv\lib\site-packages\keras\engine\training.py", line 867, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "G:\Skripsi\Program\venv\lib\site-packages\keras\engine\training.py", line 860, in run_step **
outputs = model.train_step(data)
File "G:\Skripsi\Program\venv\lib\site-packages\keras\engine\training.py", line 808, in train_step
y_pred = self(x, training=True)
File "G:\Skripsi\Program\venv\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "G:\Skripsi\Program\venv\lib\site-packages\keras\engine\input_spec.py", line 227, in assert_input_compatibility
raise ValueError(f'Input {input_index} of layer "{layer_name}" '
ValueError: Exception encountered when calling layer "model" (type Functional).
Input 0 of layer "conv2d" is incompatible with the layer: expected min_ndim=4, found ndim=1. Full shape received: (None,)
Call arguments received:
• inputs=tf.Tensor(shape=(None,), dtype=int32)
• training=True
• mask=None
这是我训练模型的代码。
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input
from densenet201 import DenseNet201
import tensorflow as tf
import pandas as pd
import numpy as np
import cv2
import os
dataset_folder = "./datasets/train_datasets"
class TrainingPreprocessing:
@staticmethod
def preprocessing_train(path):
images = cv2.imread(path, 3)
images_resize = cv2.resize(src=images, dsize=(224, 224), interpolation=cv2.INTER_LINEAR)
images_normalize = cv2.normalize(images_resize, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX,
dtype=cv2.CV_32F)
return images_normalize.reshape(224, 224, 3)
class Training:
@staticmethod
def load_data():
"""Loads and Preprocess dataset"""
train_labels_encode = []
train_labels = []
train_data = []
file_list = os.listdir(dataset_folder)
for folder in file_list:
file_list2 = os.listdir(str(dataset_folder) + '/' + str(folder))
for images in file_list2:
train_labels_encode.append(folder)
train_labels.append(folder)
train_data.append(np.array(TrainingPreprocessing.preprocessing_train(
str(dataset_folder) + '/' + str(folder) + '/' + str(images)
)))
labels = np.array(train_labels_decode)
data = np.array(train_data)
return labels, data
def split_data(self):
"""Split the preprocessed dataset to train and test data"""
x, y = self.load_data()
self.x_train, self.x_test, self.y_train, self.y_test = train_test_split(x, y, test_size=0.20, random_state=0)
print('Training data shape : ', self.x_train.shape, self.y_train.shape)
print('Testing data shape : ', self.x_test.shape, self.y_test.shape)
def train(self):
"""Compile dan fit DenseNet model"""
input_shape = 224, 224, 3
number_classes = 2
model = DenseNet201.densenet(input_shape, number_classes)
model.summary()
model.compile(loss='binary_crossentropy', optimizer='Adam', metrics=["accuracy"])
model.fit(self.x_train, self.y_train, epochs=2, verbose=1)
model.save_weights('densenet201_best_model.h5', overwrite=True)
loss, accuracy = model.evaluate(self.x_test, self.y_test)
print("[INFO] accuracy: {:.2f}%".format(accuracy * 100))
train = Training()
train.split_data()
train.train()
这是 cnn 网络的代码
from tensorflow.keras.layers import AveragePooling2D, GlobalAveragePooling2D, MaxPool2D
from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, Dense
from tensorflow.keras.layers import ReLU, concatenate, Dropout
from tensorflow.keras.models import Model
import tensorflow.keras.layers as layers
import tensorflow.keras.backend as K
import tensorflow as tf
class DenseNet201:
def densenet(image_shape, number_classes, growth_rate=32):
def batch_relu_conv(x, growth_rate, kernel=1, strides=1):
x = BatchNormalization()(x)
x = ReLU()(x)
x = Conv2D(growth_rate, kernel, strides=strides, padding='same', kernel_initializer="he_uniform")(x)
return x
def dense_block(x, repetition):
for _ in range(repetition):
y = batch_relu_conv(x, 4 * growth_rate)
y = batch_relu_conv(y, growth_rate, 3)
x = concatenate([y, x])
return x
def transition_layer(x):
x = batch_relu_conv(x, K.int_shape(x)[-1] // 2)
x = AveragePooling2D(2, strides=2, padding='same')(x)
return x
inputs = Input(image_shape)
x = Conv2D(64, 7, strides=2, padding='same', kernel_initializer="he_uniform")(inputs)
x = MaxPool2D(3, strides=2, padding='same')(x)
for repetition in [6, 12, 48, 32]:
d = dense_block(x, repetition)
x = transition_layer(d)
x = GlobalAveragePooling2D ()(d)
output = Dense(number_classes, activation='softmax')(x)
model = Model(inputs, output)
return model
您似乎在函数中反转了数据和标签（x 和 y）：
def load_data(): 其中 returns: return labels, data
我想你是在打电话model.fit(self.x_train, self.y_train, epochs=2, verbose=1)
带有标签，然后是数据。因此模型抱怨没有得到预期的数据形状。
我正在尝试训练一个 cnn 模型，但我真的不知道如何正确地训练它。我还在学习这类东西，所以我真的迷路了。我已经尝试用它做一些事情，但仍然无法理解它。有人可以向我解释如何正确地做到这一点。当我尝试将训练数据拟合到模型时，会弹出此错误。
WARNING:tensorflow:Model was constructed with shape (None, 224, 224, 3) for input KerasTensor(type_spec=TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1'), name='input_1', description="created by layer 'input_1'"), but it was called on an input with incompatible shape (None,).
Traceback (most recent call last):
File "G:/Skripsi/Program/training.py", line 80, in <module>
train.train()
File "G:/Skripsi/Program/training.py", line 70, in train
model.fit(self.x_train, self.y_train, epochs=2, verbose=1)
File "G:\Skripsi\Program\venv\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "G:\Skripsi\Program\venv\lib\site-packages\tensorflow\python\framework\func_graph.py", line 1129, in autograph_handler
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
File "G:\Skripsi\Program\venv\lib\site-packages\keras\engine\training.py", line 878, in train_function *
return step_function(self, iterator)
File "G:\Skripsi\Program\venv\lib\site-packages\keras\engine\training.py", line 867, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "G:\Skripsi\Program\venv\lib\site-packages\keras\engine\training.py", line 860, in run_step **
outputs = model.train_step(data)
File "G:\Skripsi\Program\venv\lib\site-packages\keras\engine\training.py", line 808, in train_step
y_pred = self(x, training=True)
File "G:\Skripsi\Program\venv\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "G:\Skripsi\Program\venv\lib\site-packages\keras\engine\input_spec.py", line 227, in assert_input_compatibility
raise ValueError(f'Input {input_index} of layer "{layer_name}" '
ValueError: Exception encountered when calling layer "model" (type Functional).
Input 0 of layer "conv2d" is incompatible with the layer: expected min_ndim=4, found ndim=1. Full shape received: (None,)
Call arguments received:
• inputs=tf.Tensor(shape=(None,), dtype=int32)
• training=True
• mask=None
这是我训练模型的代码。
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input
from densenet201 import DenseNet201
import tensorflow as tf
import pandas as pd
import numpy as np
import cv2
import os
dataset_folder = "./datasets/train_datasets"
class TrainingPreprocessing:
@staticmethod
def preprocessing_train(path):
images = cv2.imread(path, 3)
images_resize = cv2.resize(src=images, dsize=(224, 224), interpolation=cv2.INTER_LINEAR)
images_normalize = cv2.normalize(images_resize, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX,
dtype=cv2.CV_32F)
return images_normalize.reshape(224, 224, 3)
class Training:
@staticmethod
def load_data():
"""Loads and Preprocess dataset"""
train_labels_encode = []
train_labels = []
train_data = []
file_list = os.listdir(dataset_folder)
for folder in file_list:
file_list2 = os.listdir(str(dataset_folder) + '/' + str(folder))
for images in file_list2:
train_labels_encode.append(folder)
train_labels.append(folder)
train_data.append(np.array(TrainingPreprocessing.preprocessing_train(
str(dataset_folder) + '/' + str(folder) + '/' + str(images)
)))
labels = np.array(train_labels_decode)
data = np.array(train_data)
return labels, data
def split_data(self):
"""Split the preprocessed dataset to train and test data"""
x, y = self.load_data()
self.x_train, self.x_test, self.y_train, self.y_test = train_test_split(x, y, test_size=0.20, random_state=0)
print('Training data shape : ', self.x_train.shape, self.y_train.shape)
print('Testing data shape : ', self.x_test.shape, self.y_test.shape)
def train(self):
"""Compile dan fit DenseNet model"""
input_shape = 224, 224, 3
number_classes = 2
model = DenseNet201.densenet(input_shape, number_classes)
model.summary()
model.compile(loss='binary_crossentropy', optimizer='Adam', metrics=["accuracy"])
model.fit(self.x_train, self.y_train, epochs=2, verbose=1)
model.save_weights('densenet201_best_model.h5', overwrite=True)
loss, accuracy = model.evaluate(self.x_test, self.y_test)
print("[INFO] accuracy: {:.2f}%".format(accuracy * 100))
train = Training()
train.split_data()
train.train()
这是 cnn 网络的代码
from tensorflow.keras.layers import AveragePooling2D, GlobalAveragePooling2D, MaxPool2D
from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, Dense
from tensorflow.keras.layers import ReLU, concatenate, Dropout
from tensorflow.keras.models import Model
import tensorflow.keras.layers as layers
import tensorflow.keras.backend as K
import tensorflow as tf
class DenseNet201:
def densenet(image_shape, number_classes, growth_rate=32):
def batch_relu_conv(x, growth_rate, kernel=1, strides=1):
x = BatchNormalization()(x)
x = ReLU()(x)
x = Conv2D(growth_rate, kernel, strides=strides, padding='same', kernel_initializer="he_uniform")(x)
return x
def dense_block(x, repetition):
for _ in range(repetition):
y = batch_relu_conv(x, 4 * growth_rate)
y = batch_relu_conv(y, growth_rate, 3)
x = concatenate([y, x])
return x
def transition_layer(x):
x = batch_relu_conv(x, K.int_shape(x)[-1] // 2)
x = AveragePooling2D(2, strides=2, padding='same')(x)
return x
inputs = Input(image_shape)
x = Conv2D(64, 7, strides=2, padding='same', kernel_initializer="he_uniform")(inputs)
x = MaxPool2D(3, strides=2, padding='same')(x)
for repetition in [6, 12, 48, 32]:
d = dense_block(x, repetition)
x = transition_layer(d)
x = GlobalAveragePooling2D ()(d)
output = Dense(number_classes, activation='softmax')(x)
model = Model(inputs, output)
return model
您似乎在函数中反转了数据和标签（x 和 y）：
def load_data(): 其中 returns: return labels, data
我想你是在打电话model.fit(self.x_train, self.y_train, epochs=2, verbose=1)
带有标签，然后是数据。因此模型抱怨没有得到预期的数据形状。