(첫글) Python/Deep Learning 질문게시판입니다.

[codingapple]

누구나 작성할 수 있습니다.

[늦지않았다]

대학원 합격 예측 시 사용된 csv 파일은 어떻게 받을 수 있는지요?

[codingapple]

영상 하단에 첨부파일이 있습니다 

[Chagon Son]

import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np

(trainX, trainY), (testX, testY) = tf.keras.datasets.fashion_mnist.load_data()

trainX = trainX / 255.0
trainY = trainY / 255.0

trainX = trainX.reshape ( (trainX.shape[0], 28, 28, 1))
trainY = testX.reshape ( (testX.shape[0], 28, 28, 1))

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

#print(trainX[0])
#print(trainX.shape)

#print(testX[0])
#print(testX.shape)

#print(trainY)

#plt.imshow(trainX[1])
#plt.gray()
#plt.colorbar()
#plt.show()

model = tf.keras.Sequential([
                            tf.keras.layers.Conv2D( 32, (3, 3), padding="same", activation="relu", input_shape=(28, 28, 1) ),
                            tf.keras.layers.MaxPooling2D( (2,2) ),
                            # tf.keras.layers.Dense(128, input_shape=(28, 28), activation="relu"),
                            tf.keras.layers.Flatten(),
                            tf.keras.layers.Dense(64, activation="relu"),
                            tf.keras.layers.Dense(10, activation="softmax"),

])

model.summary()

model.compile(loss="sparse_categorical_crossentropy", optimizer="adam", metrics=['accuracy'])

print(trainX[0])

model.fit(trainX, trainY, validation_data=(testX, testY), epochs=5)

score = model.evaluate(testX, testY)

 

제가 강의에 따라서 작성한 코드입니다.

실행하면 아래와  같은 에러가 발생합니다.

무엇이 문제인가요?

<hr />

Model: "sequential_10"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_9 (Conv2D) (None, 28, 28, 32) 320
_________________________________________________________________
max_pooling2d_9 (MaxPooling2 (None, 14, 14, 32) 0
_________________________________________________________________
flatten_10 (Flatten) (None, 6272) 0
_________________________________________________________________
dense_21 (Dense) (None, 64) 401472
_________________________________________________________________
dense_22 (Dense) (None, 10) 650
=================================================================
Total params: 402,442
Trainable params: 402,442
Non-trainable params: 0
_________________________________________________________________
AAAAAAAAAAAAAAAAAAAAAAAAAA

---------------------------------------------------------------------------

ValueError Traceback (most recent call last)

<ipython-input-11-968f22cba899> in <module>()
46 print("AAAAAAAAAAAAAAAAAAAAAAAAAA")
47
---> 48 model.fit(trainX, trainY, validation_data=(testX, testY), epochs=5)
49
50 score = model.evaluate(testX, testY)

3 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/data_adapter.py in _check_data_cardinality(data)
1527 label, ", ".join(str(i.shape[0]) for i in nest.flatten(single_data)))
1528 msg += "Make sure all arrays contain the same number of samples."
-> 1529 raise ValueError(msg)
1530
1531

ValueError: Data cardinality is ambiguous:
x sizes: 60000
y sizes: 10000
Make sure all arrays contain the same number of samples.

<hr />

 

 

[글쓴이]

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