62 lines
2.7 KiB
Python
Executable File
62 lines
2.7 KiB
Python
Executable File
'''
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conv_1 = slim.conv2d(images, 64, [3, 3], 1, padding='SAME', scope='conv1')
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# (inputs,num_outputs,[卷积核个数] kernel_size,[卷积核的高度，卷积核的宽]stride=1,padding='SAME',)
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max_pool_1 = slim.max_pool2d(conv_1, [2, 2], [2, 2], padding='SAME')
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conv_2 = slim.conv2d(max_pool_1, 128, [3, 3], padding='SAME', scope='conv2')
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max_pool_2 = slim.max_pool2d(conv_2, [2, 2], [2, 2], padding='SAME')
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conv_3 = slim.conv2d(max_pool_2, 256, [3, 3], padding='SAME', scope='conv3')
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max_pool_3 = slim.max_pool2d(conv_3, [2, 2], [2, 2], padding='SAME')
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flatten = slim.flatten(max_pool_3)
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fc1 = slim.fully_connected(tf.nn.dropout(flatten, keep_prob), 1024, activation_fn=tf.nn.tanh, scope='fc1')
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logits = slim.fully_connected(tf.nn.dropout(fc1, keep_prob), FLAGS.charset_size, activation_fn=None, scope='fc2')
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# logits = slim.fully_connected(flatten, FLAGS.charset_size, activation_fn=None, reuse=reuse, scope='fc')
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loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels))
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# y表示的是实际类别，y_表示预测结果，这实际上面是把原来的神经网络输出层的softmax和cross_entrop何在一起计算，为了追求速度
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accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits, 1), labels), tf.float32))
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'''
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import tensorflow as tf
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from tensorflow.keras import layers
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# some simple models
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def build_net_001(input_shape, n_classes):
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assert len(input_shape) == 3, 'only support 3 channels'
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model = tf.keras.Sequential()
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model.add(tf.keras.layers.Conv2D(
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input_shape=input_shape, filters=32, kernel_size=(3, 3), strides=(1, 1),
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padding='valid', activation='relu'))
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model.add(tf.keras.layers.MaxPool2D(pool_size=(2, 2)))
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model.add(tf.keras.layers.Flatten())
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model.add(tf.keras.layers.Dense(32, activation='relu'))
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model.add(tf.keras.layers.Dense(n_classes, activation='softmax'))
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return model
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def build_net_002(input_shape, n_classes):
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model = tf.keras.Sequential([
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layers.Conv2D(input_shape=input_shape, filters=64, kernel_size=(3, 3), strides=(1, 1),
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padding='same', activation='relu'),
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layers.MaxPool2D(pool_size=(2, 2), padding='same'),
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layers.Conv2D(filters=128, kernel_size=(3, 3), padding='same'),
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layers.MaxPool2D(pool_size=(2, 2), padding='same'),
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layers.Conv2D(filters=256, kernel_size=(3, 3), padding='same'),
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layers.MaxPool2D(pool_size=(2, 2), padding='same'),
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layers.Flatten(),
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layers.Dense(1024, activation='relu'),
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layers.Dense(n_classes, activation='softmax')
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])
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return model
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# some models wrapped into tf.keras.Model
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class CNNNet(tf.keras.Model):
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def __init__(self):
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pass
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