Question

我在这里问过：Tensorflow, expected conv2d_input to have 4 dimensions

新问题是：在我适应了机器加工之后，我试图预测一个新机器。作为名为demo1.jpg的图像

我期望获得什么新功能。进入我的图书馆：

我正在使用tf.keras，并且遇到以下错误：

ValueError: Error when checking input: expected conv2d_input to have 4 dimensions, but got array with shape (1, 1)

我收到新错误，然后进入模式。预测一个新的（功能）：

import tensorflow as tf
import numpy as np
import pickle
import cv2
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D

IMG_SIZE = 50

def prepare(file):
    img_array = cv2.imread(file, cv2.IMREAD_GRAYSCALE)
    new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))

    predictdata = tf.reshape(new_array, (1, 50, 50))
    predictdata = np.expand_dims(predictdata, -1)
    return predictdata


pickle_ind = open("x.pickle", "rb")
x = pickle.load(pickle_ind)
x = np.array(x, dtype=float)
x = np.expand_dims(x, -1)

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.add(Flatten())
model.add(Dense(1, activation='softmax'))

model.summary()

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

model.fit(x, y, epochs=1, batch_size=n_batch)
prediction = model.predict([prepare('demo1.jpg')], batch_size=n_batch, steps=1, verbose=1)

print(prediction)

Tensorflow，期望conv2d_input具有4个维，形状为（1，1）

0 个答案: