Q&A
自分も同じサイトを参考にし，同じエラーが発生したためどのように解決されたか教えていただけないでしょうか？？
ValueError: Input 0 of layer conv2d_7 is incompatible with the layer: : expected min_ndim=4, found ndim=1. Full shape received: [None]
というerrorがでましたが、なにが原因でerrorが出ているのかこの文章から理解できません。このerrorは、mainという関数を呼び出した時に出てきました。Errorの原因と、その直し方を教えてください。
(環境：google Colaboratory)
from PIL import Image
import os, glob
import numpy as np
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
classes = ["Jomon", "Yayoi"]
num_classes = len(classes)
image_size = 64
num_testdata =25
x_train =[]
x_test = []
y_train = []
y_test = []
for index, classlabel in enumerate(classes):
photos_dir = "./"+ classlabel
files = glob.glob(photos_dir + "/*.jpg")
for i, file in enumerate(files):
image = Image.open(file)
image = image.convert("RGB")
image = image.resize((image_size, image_size))
data = np.asarray(image)
if i < num_testdata:
x_test.append(data)
y_test.append(index)
for angle in range(-20, 20, 5):
img_r = image.rotate(angle)
data = np.asarray(img_r)
x_train.append(data)
y_train.append(index)
img_trains = img_r.transpose(Image.FLIP_LEFT_RIGHT) data = np.asarray(img_trains) x_train.append(data) y_train.append(index)
x_train = np.array(x_train)
x_test = np.array(x_test)
y_train = np.array(y_train)
y_test = np.array(y_test)
xy = (x_train, x_test, y_train, y_test)
np.save("./alien_exam.npy", xy)
from keras.models import Sequential#a plain stack of layers where each layer has exactly one input tensor and one output tensor.
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.optimizers import RMSprop
from keras.utils import np_utils
import keras
import numpy as np
classes = ["Jomon", "Yayoi"]
num_classes = len(classes)
image_size = 64
"""
データを読み込み
"""
def load_data():
x_train, x_test, y_train, y_test = np.load("./alien_exam.npy", allow_pickle=True)
x_train = x_train.astype("float") /255
x_test = x_test.astype("float") /255
y_train = np_utils.to_categorical(y_train, num_classes)
y_tets = np_utils.to_categorical(y_test, num_classes)
return x_train, y_train, x_test, y_test
def train(x, y, x_test, y_test):
model = Sequential()
model.add(Conv2D(32,(3,3), padding="same",input_shape=x.shape[1:]))
model.add(Acticaiton("relu"))
model.add(Conv2D(32,(3,3)))
model.add(Acticaiton("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation("relu"))
model.add(Dropout(0.45))
model.add(Dense(2))
model.add(Activation("softmax"))
opt = RMSprop(lr=0.00005, decay=le-6)
model.compile(lostt="categorical_cossentropy", optimizer=opt, metrics=["accuracy"])
model.fit(x, y, batch_size=28, epochs=40)
model.save("./cnn.h1")
return model
def main():
x_train, y_train, x_test, y_test = load_data()
model = train(x_train, y_train, x_test, y_test)
main()
#ここでErrorがでました。
回答1件
0
ベストアンサー
まず、 Activation のつづりを修正したうえで input_shape に適当な形状を指定してモデルの構造を可視化してみます。
python
1from keras.models import Sequential 2from keras.layers import Conv2D, MaxPooling2D 3from keras.layers import Activation, Dropout, Flatten, Dense 4from keras.optimizers import RMSprop 5from keras.utils import np_utils 6import keras 7import numpy as np 8 9classes = ["Jomon", "Yayoi"] 10num_classes = len(classes) 11image_size = 64 12 13model = Sequential() 14 15model.add(Conv2D(32,(3,3), padding="same",input_shape=(256,256,3))) 16model.add(Activation("relu")) 17model.add(Conv2D(32,(3,3))) 18model.add(Activation("relu")) 19model.add(MaxPooling2D(pool_size=(2,2))) 20model.add(Dropout(0.25)) 21 22model.add(Flatten()) 23model.add(Dense(512)) 24model.add(Activation("relu")) 25model.add(Dropout(0.45)) 26model.add(Dense(2)) 27model.add(Activation("softmax")) 28 29model.summary()
すると下記のように表示されます。
Model: "sequential_3" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_2 (Conv2D) (None, 256, 256, 32) 896 _________________________________________________________________ activation (Activation) (None, 256, 256, 32) 0 _________________________________________________________________ conv2d_3 (Conv2D) (None, 254, 254, 32) 9248 _________________________________________________________________ activation_1 (Activation) (None, 254, 254, 32) 0 _________________________________________________________________ max_pooling2d (MaxPooling2D) (None, 127, 127, 32) 0 _________________________________________________________________ dropout (Dropout) (None, 127, 127, 32) 0 _________________________________________________________________ flatten (Flatten) (None, 516128) 0 _________________________________________________________________ dense (Dense) (None, 512) 264258048 _________________________________________________________________ activation_2 (Activation) (None, 512) 0 _________________________________________________________________ dropout_1 (Dropout) (None, 512) 0 _________________________________________________________________ dense_1 (Dense) (None, 2) 1026 _________________________________________________________________ activation_3 (Activation) (None, 2) 0 ================================================================= Total params: 264,269,218 Trainable params: 264,269,218 Non-trainable params: 0 _________________________________________________________________
では、試しに入力データをわざと None にしてみると下記のようにエラーになります。
python
1from keras.models import Sequential 2from keras.layers import Conv2D, MaxPooling2D 3from keras.layers import Activation, Dropout, Flatten, Dense 4from keras.optimizers import RMSprop 5from keras.utils import np_utils 6import keras 7import numpy as np 8 9classes = ["Jomon", "Yayoi"] 10num_classes = len(classes) 11image_size = 64 12 13model = Sequential() 14 15model.add(Conv2D(32,(3,3), padding="same",input_shape=([None]))) 16model.add(Activation("relu")) 17model.add(Conv2D(32,(3,3))) 18model.add(Activation("relu")) 19model.add(MaxPooling2D(pool_size=(2,2))) 20model.add(Dropout(0.25)) 21 22model.add(Flatten()) 23model.add(Dense(512)) 24model.add(Activation("relu")) 25model.add(Dropout(0.45)) 26model.add(Dense(2)) 27model.add(Activation("softmax")) 28 29model.summary()
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-8-3e74efe4338a> in <module>() 13 model = Sequential() 14 ---> 15 model.add(Conv2D(32,(3,3), padding="same",input_shape=([None]))) 16 model.add(Activation("relu")) 17 model.add(Conv2D(32,(3,3))) 4 frames /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/input_spec.py in assert_input_compatibility(input_spec, inputs, layer_name) 194 ', found ndim=' + str(ndim) + 195 '. Full shape received: ' + --> 196 str(x.shape.as_list())) 197 # Check dtype. 198 if spec.dtype is not None: ValueError: Input 0 of layer conv2d_7 is incompatible with the layer: : expected min_ndim=4, found ndim=2. Full shape received: [None, None]
考えられる原因としては、x_train = x_train.astype("float") /255 で読み込んでいるデータを input_shape=x.shape[1:] で読み込んだときに空っぽになっているのではないでしょうか。入力として期待している次元数 4 例えば [1,256,256,3] のような四次元に対して、次元数 1 例えば [None] のような一次元のデータが入力されているということです。
投稿2020/09/12 08:17
総合スコア351
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