कैसे फिट करने के लिए छवि डेटा सही ढंग से करने के लिए एक मॉडल में अजगर?

0

सवाल

मैं कोशिश कर रहा हूँ करने के लिए प्रशिक्षित एक सीएनएन मॉडल है, लेकिन मैं वास्तव में समझ में नहीं आता कि कैसे इसे ठीक करने के लिए. मैं अभी भी सीखने के बारे में इस तरह की चीजें तो मैं कर रहा हूँ वास्तव में खो दिया है । मैं पहले से ही की कोशिश की सामान कर रही के साथ, लेकिन यह अभी भी प्राप्त नहीं कर सकता, मेरे सिर के चारों ओर यह. कोई मुझे बता सकते हैं कैसे इसे ठीक करने के लिए. जब मैं कोशिश फिट करने के लिए प्रशिक्षित करने के लिए डेटा मॉडल इस त्रुटि को चबूतरे.

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

यह है मेरे कोड के लिए प्रशिक्षण मॉडल.

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()

और यह कोड के लिए सीएनएन नेटवर्क

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

सबसे अच्छा जवाब

0

यह लगता है आप उल्टे डेटा और लेबल (x और y) समारोह में:

def load_data(): जो देता है: return labels, data

मुझे लगता है कि आप बुला रहे हैं model.fit(self.x_train, self.y_train, epochs=2, verbose=1) के साथ लेबल और फिर डेटा. इसलिए मॉडल के बारे में शिकायत नहीं की उम्मीद है और डेटा आकार.

2021-11-24 15:14:21

अन्य भाषाओं में

यह पृष्ठ अन्य भाषाओं में है

Русский
..................................................................................................................
Italiano
..................................................................................................................
Polski
..................................................................................................................
Română
..................................................................................................................
한국어
..................................................................................................................
Français
..................................................................................................................
Türk
..................................................................................................................
Česk
..................................................................................................................
Português
..................................................................................................................
ไทย
..................................................................................................................
中文
..................................................................................................................
Español
..................................................................................................................
Slovenský
..................................................................................................................

इस श्रेणी में लोकप्रिय

लोकप्रिय सवाल इस श्रेणी में