本文介绍了Tensorflow,预期conv2d_input具有4个维度的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
我正在使用tf.keras
,并且出现以下错误:
I'm using tf.keras
and I'm getting following error:
有人可以帮我吗?
代码(Image_Size为:50x50
)
Code (Image_Size is: 50x50
)
import tensorflow as tf
import numpy as np
import pickle
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
pickle_ind = open("x.pickle", "rb")
x = pickle.load(pickle_ind)
x = np.array(x, dtype=float)
# x = x/255.0
pickle_ind = open("y.pickle", "rb")
y = pickle.load(pickle_ind)
n_batch = len(x)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(50, 50, 1)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.summary()
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
model.fit(x, y, epochs=20, batch_size=n_batch)
推荐答案
添加channels
尺寸:
x = np.expand_dims(x, -1)
您还需要添加输出密集层:
You also need to add output dense layer:
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(50, 50, 1)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Flatten())
model.add(Dense(2, activation='softmax'))
model.compile(optimizer='adam',
loss='sparse_softmax_crossentropy',
metrics=['accuracy'])
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