我收到一個錯誤。它說：
Model was constructed with shape (None, 28) for input KerasTensor(type_spec=TensorSpec(shape=(None, 28), dtype=tf.float32, name='dense_45_input'), name='dense_45_input', description="created by layer 'dense_45_input'"), but it was called on an input with incompatible shape (None, 28, 28).
我的代碼在這里：
from keras.utils import np_utils
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, BatchNormalization, Dropout, Activation
import seaborn as sns
from keras.initializers import RandomNormal
from keras.initializers import he_normal
import matplotlib.pyplot as plt
(train_X, train_y), (test_X, test_y) = mnist.load_data()
output_dim = 10
input_dim = train_X.shape[1]
batch_size = 128
nb_epoch = 20
model_drop = Sequential()
model_drop.add(Dense(512, activation='relu', input_shape=(input_dim,),kernel_initializer=he_normal(seed=None)))
model_drop.add(BatchNormalization())
model_drop.add(Dropout(0.5))
model_drop.add(Dense(128, activation= 'relu', kernel_initializer=he_normal(seed=None)))
model_drop.add(BatchNormalization())
model_drop.add(Dropout(0.5))
model_drop.add(Dense(output_dim, activation = 'softmax'))
model_drop.summary()
model_drop.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
history = model_drop.fit(train_X, train_y, batch_size=batch_size, epochs=nb_epoch, verbose=1)
我怎樣才能解決這個問題？我也在添加錯誤照片..
uj5u.com熱心網友回復：
您在構建密集層時的輸入尺寸是錯誤的，如果影像是 28x28，您需要能夠接收所有像素（即您需要 28*28=784 個輸入連接）。要真正實作這一點，您還需要對 y 變數進行單熱編碼并重塑影像。
(train_X, train_y), (test_X, test_y) = mnist.load_data()
output_dim = 10
input_dim = train_X.shape[1]
batch_size = 128
nb_epoch = 20
model_drop = Sequential()
# see input_dim edit here
model_drop.add(Dense(512, activation='relu', input_shape=(input_dim*input_dim,),kernel_initializer=he_normal(seed=None)))
model_drop.add(BatchNormalization())
model_drop.add(Dropout(0.5))
model_drop.add(Dense(128, activation= 'relu', kernel_initializer=he_normal(seed=None)))
model_drop.add(BatchNormalization())
model_drop.add(Dropout(0.5))
model_drop.add(Dense(output_dim, activation = 'softmax'))
model_drop.summary()
model_drop.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# encode Y_train and also shape X_train so it can feed to dense layer
Y_train = np_utils.to_categorical(train_y, num_classes=10)
X_train = train_X.reshape((-1, 28*28))
history = model_drop.fit(X_train, Y_train, batch_size=batch_size, epochs=nb_epoch, verbose=1)
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標籤：Python 机器学习 凯拉斯 谷歌合作实验室 感知器
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