我试图将图像预测到我的预训练深度学习分类模型

Question

When I test with validation data the output returned be with that shape [7.2918165e-06 3.2451030e-02 9.6753991e-01 1.7616502e-06] when I try to send a single image after I save the model and predict it the output be in shape [0. 1. 0. 0.] what is the problem

Answer 1

With the model.predict( ) is supposed to return the same shape as the model output with the valid values co-responsive from the input, and current training weights with a small change in the process when it is trainable.

The output of the last layer with activation, target layer output absorbed optimizer or loss Fn. Some of the output is binary, entropy, logits, or multiple values when you working with layer output. ( see the shape is [ 2, 1 ] )

[ Sample ]:

from os.path import exists

import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
None
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
physical_devices = tf.config.experimental.list_physical_devices('GPU')
assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
config = tf.config.experimental.set_memory_growth(physical_devices[0], True)
print(physical_devices)
print(config)   

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
learning_rate = 0.001
global_step = 0
vocabulary_size = 5000
start = 0
limit = 128
delta = 1
embedding_size = 16
n_sample = 16

tf.compat.v1.disable_eager_execution()

# Input data.
inputs = tf.compat.v1.get_variable('X', dtype = tf.int32, initializer = tf.random.uniform(shape=[1], maxval=1, dtype=tf.int32, seed=10))
labels = tf.compat.v1.get_variable('Y', dtype = tf.int32, initializer = tf.random.uniform(shape=[1, 1], maxval=1, dtype=tf.int32, seed=10))

# Look up embeddings for inputs.
embeddings = tf.Variable(
tf.random.uniform([vocabulary_size, embedding_size], -1.0, 1.0)
)
embed = tf.nn.embedding_lookup(embeddings, inputs)

# Construct the variables for the NCE loss
nce_weights = tf.Variable(
    tf.random.uniform(shape=[vocabulary_size, embedding_size], maxval=255, dtype=tf.float32,)
)
nce_biases = tf.Variable(tf.zeros([vocabulary_size]))

# time we evaluate the loss.
loss = tf.reduce_mean(
tf.nn.nce_loss(nce_weights, nce_biases,
               labels=labels,
               inputs=embed,
               num_sampled=n_sample,
               num_classes=vocabulary_size),
name='loss'
)
optimizer = tf.compat.v1.train.ProximalAdagradOptimizer(
    learning_rate,
    initial_accumulator_value=0.1,
    l1_regularization_strength=0.2,
    l2_regularization_strength=0.1,
    use_locking=False,
    name='ProximalAdagrad'
)
training_op = optimizer.minimize(loss, name='minimize') 


"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: DataSet / Input
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
X = np.reshape([ 500 ], (1))
Y = np.reshape([ 15 ], (1, 1))
history = [ ] 

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Training / Optimize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
with tf.compat.v1.Session() as sess:
    sess.run(tf.compat.v1.global_variables_initializer())
    
    
    first_value = 500
    second_value = 15
    for i in range(1000):
        global_step = global_step + 1
        train_loss, temp = sess.run([loss, training_op], feed_dict={inputs:X, labels:Y})
        history.append(train_loss)
        
        print( 'steps: ' + str(i) )
        
        a = tf.constant([ first_value ])
        b = tf.constant([ second_value ])
        v = sess.run( [a, b] )
        print( v )
        
        first_value = first_value - i
        second_value = second_value - i
        
sess.close()

print( history )
plt.plot(history)
plt.show()
plt.close()

input('...')

[ Output ]: