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machine-learning - CNN 不兼容

问题描述

我的数据具有以下形状：

 X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)
    print(X_train.shape, X_test.shape, Y_train.shape, Y_test.shape)
(942, 32, 32, 1) (236, 32, 32, 1) (942, 3, 3) (236, 3, 3)

每当我尝试运行我的 CNN 时，我都会收到以下错误：

from tensorflow.keras import layers
from tensorflow.keras import Model
img_input = layers.Input(shape=(32, 32, 1))

x = layers.Conv2D(16, (3,3), activation='relu', strides = 1, padding = 'same')(img_input)
x = layers.Conv2D(32, (3,3), activation='relu', strides = 2)(x)
x = layers.Conv2D(128, (3,3), activation='relu', strides = 2)(x)
x = layers.MaxPool2D(pool_size=2)(x)
x = layers.Conv2D(3, 3, activation='linear', strides = 2)(x)


output = layers.Flatten()(x)

model = Model(img_input, output)
model.summary()

model.compile(loss='mean_squared_error',optimizer= 'adam', metrics=['mse'])

history = model.fit(X_train,Y_train,validation_data=(X_test, Y_test), epochs = 100,verbose=1)

错误：

InvalidArgumentError:  Incompatible shapes: [32,3] vs. [32,3,3]
     [[node BroadcastGradientArgs_2 (defined at /usr/local/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py:1751) ]] [Op:__inference_distributed_function_7567]

Function call stack:
distributed_function

我在这里想念什么？

标签： machine-learningkerasneural-networkconv-neural-network

解决方案

您没有正确处理网络内部的维度。首先扩展您的 y 的维度，以便以这种格式 (n_sample, 3, 3, 1) 获取它们。此时调整网络（我去掉 flatten 和 max pooling 并调整最后一个 conv 输出）

# create dummy data
n_sample = 10
X = np.random.uniform(0,1, (n_sample, 32, 32, 1))
y = np.random.uniform(0,1, (n_sample, 3, 3))

# expand y dim
y = y[...,np.newaxis]
print(X.shape, y.shape)

img_input = Input(shape=(32, 32, 1))

x = Conv2D(16, (3,3), activation='relu', strides = 1, padding = 'same')(img_input)
x = Conv2D(32, (3,3), activation='relu', strides = 2)(x)
x = Conv2D(128, (3,3), activation='relu', strides = 2)(x)
x = Conv2D(1, (3,3), activation='linear', strides = 2)(x)

model = Model(img_input, x)
model.summary()

model.compile(loss='mean_squared_error',optimizer= 'adam', metrics=['mse'])
model.fit(X,y, epochs=3)

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