如何适合的图像数据正确的模型在蟒蛇?

0

的问题

我想到经过培训的一个有线电视新闻网的模型,但是我真的不了解如何做到这一点正确。 我仍然在学习有关这种东西所以我真的丢失。 我已经试图这样做的东西,但仍然无法获得我的头在它附近。 可以有人向我解释如何做到这一点正确。 当我尝试适应的火车数据的模型这个错误。

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

这是我的代码,用于培训的模式。

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()

这是码有线电视新闻网的网络

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
deep-learning keras python tensorflow
2021-11-24 06:49:28
1

最好的答案

0

看来你倒数据和标签(x和y)在功能:

def load_data(): 其回报: return labels, data

我觉得你打电话 model.fit(self.x_train, self.y_train, epochs=2, verbose=1) 与标签,然后数据。 因此,模型抱怨没有得到预期的数据的形状。

2021-11-24 15:14:21

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