私は ML とコンピューター ビジョンにかなり慣れていません。猫/犬 0 を猫、1 を犬として分類しようとしています。しかし、私の model.fit() 関数はこのエラーを吐き出します。
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ValueError: Input 0 of layer sequential_5 is incompatible with the layer: : expected min_ndim=4, found ndim=2. Full shape received: [None, 10000]
これは私の ML モデルです。
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import cv2
#the images are stored in the Folders 'Cat/' and 'Dog/'
animal = ['Cat/','Dog/']
images_cat= []
images_dog=[]
# reads in the images
for x in animal:
for i in range(1,12500): # the are images from '1.jpg' till '12499.jpg' for each Cats and Dogs
try:
image_path = x+ str(i) +'.jpg'# this gets the path of the images for example 'Cat/1.jpg'
#print(image_path)
img = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2GRAY)
img_resized = cv2.resize(img,dsize=(100,100))
if x == 'Cat/':
images_cat.append(img_resized)
elif x == 'Dog/':
images_dog.append(img_resized)
except cv2.error as e:
#some images spit out an errer and the apprently can't be read so therefore I just give them the first image to add to the list
if x == 'Cat/':
images_cat.append(images_cat[1])
elif x == 'Dog/':
images_dog.append(images_dog[1])
# assign targets to values
y_cat = np.zeros(len(images_cat)) # Cat == 0
y_dog = np.ones(len(images_dog)) # Dog == 1
# trainig_images = 80% test_images= 20%
training_sample_count = round(0.8* len(y_cat))
#list slicing the images to get 80% of the images as calculated above
X_cat_train = images_cat [:training_sample_count]
y_cat_train_fin = y_cat[:training_sample_count]
X_dog_train = images_dog [:training_sample_count]
y_dog_train_fin = y_dog[:training_sample_count]
# create the final training list
X_train = X_cat_train + X_dog_train
y_train=[]
y_train.append(y_cat_train_fin.data)
y_train.append(y_dog_train_fin.data)
y_train = np.reshape(y_train,(19998,))
np.shape(y_train)# output: (19998,)
#normalizing the data
X_train = [x / 255.0 for x in X_train]
X_train = np.reshape(X_train,(19998,10000))
np.shape(X_train) #output: (19998, 10000)
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, Dropout, MaxPooling2D, Conv2D, Flatten
model = Sequential()
model.add(Conv2D(32,kernel_size=(5,5),padding='same', activation ='relu'))
model.add(MaxPooling2D((3,3)))
model.add(Conv2D(32,kernel_size=(5,5),padding='same', activation ='relu'))
model.add(MaxPooling2D((3,3)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1, activation='softmax'))
model.compile(optimizer='adam', loss="sparse_categorical_crossentropy", metrics=["accuracy"])
model.fit(
X_train,
y_train,
epochs=10,
batch_size=10000)
私はまだイメージのテストを行っていませんが、基本的には、将来のデータ (猫や犬のイメージを予測するなど) のためにこのモデルをトレーニングしようとします。atmで立ち往生しているので、誰かが私の問題を解決してくれると嬉しいです. 乾杯 :)