我需要一些關于我的機器學習代碼的幫助。我正在使用谷歌預訓練模型的基礎，并添加我自己的最后一個展平和輸出層。
訓練模型并保存后，我將代碼中的訓練部分注釋掉，加載并使用保存的模型。
我的代碼：
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
import tensorflow_datasets as tfds
tfds.disable_progress_bar()
class_names = ['cat','dog']
#load the images
(raw_train, raw_validation, raw_test), metadata = tfds.load(
'cats_vs_dogs',
split=['train[:80%]', 'train[80%:90%]', 'train[90%:]'],
with_info=True,
as_supervised=True,
)
get_label_name = metadata.features['label'].int2str
#function to resize image
IMG_SIZE=160
def format_example(image, label):
image = tf.cast(image, tf.float32) #cast to convert integer values in pixels to float32
image = (image/127.5) - 1
image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE))
return image,label
#.map is to apply a function to all raw images
train = raw_train.map(format_example)
validation = raw_validation.map(format_example)
test = raw_test.map(format_example)
BATCH_SIZE = 32
SHUFFLE_BUFFER_SIZE = 1000
train_batches = train.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)
validation_batches = validation.batch(BATCH_SIZE)
test_batches = test.batch(BATCH_SIZE)
IMG_SHAPE = (IMG_SIZE, IMG_SIZE, 3)
#Create base model from pretrained model MobileNetV2
base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,
include_top=False,
weights='imagenet')
#freezing the base (preventing any more training)
base_model.trainable = False
#Flattening it down
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
#Adding dense layer of only 1 neuron as only 2 possibilities(cat/dog)
prediction_layer = keras.layers.Dense(1)
model = tf.keras.Sequential([
base_model,
global_average_layer,
prediction_layer
])
#slow learning rate since it is a pretrained model
base_learning_rate = 0.0001
#Compiling the model
model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=base_learning_rate),
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=['accuracy'])
initial_epochs = 3
validation_steps=20
loss0,accuracy0 = model.evaluate(validation_batches, steps = validation_steps)
print('training...')
history = model.fit(train_batches,
epochs=initial_epochs,
validation_data = validation_batches)
acc = history.history['accuracy']
print(acc)
model.save("dogs_vs_cats.h5")
model = tf.keras.models.load_model('dogs_vs_cats.h5', compile=True)
print('predicting... ')
prediction = model.predict(test, verbose=1)
print(prediction)
我收到以下錯誤：
ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 160, 160, 3), found shape=(160, 160, 3)
uj5u.com熱心網友回復：
我是否正確理解只有在您評論培訓代碼并從 開始運行行之后才會出現此錯誤model = tf.keras.models.load_model('dogs_vs_cats.h5', compile=True)？如果是，我認為你在這一行有一個錯誤：
prediction = model.predict(test, verbose=1)
您使用test3D 影像，但不使用 4D 影像進行批處理test_batches，這就是您獲得 3D/4D 形狀錯誤的原因。
轉載請註明出處，本文鏈接：https://www.uj5u.com/gongcheng/465899.html
標籤：Python 机器学习 喀拉斯 卷积神经网络 张量流2.0