Ejemplos de test_nn_image_inputs en Python

Ejemplo n.º 1

Archivo: image_classification5.ipynb.py Proyecto: maranemil/howto

def neural_net_keep_prob_input():
    """
    Return a Tensor for keep probability
    : return: Tensor for keep probability.
    """
    # TODO: Implement Function
    return tf.placeholder(tf.float32, name='keep_prob')



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tf.reset_default_graph()
tests.test_nn_image_inputs(neural_net_image_input)
tests.test_nn_label_inputs(neural_net_label_input)
tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)


# ### Convolution and Max Pooling Layer
# Convolution layers have a lot of success with images. For this code cell, you should implement the function `conv2d_maxpool` to apply convolution then max pooling:
# * Create the weight and bias using `conv_ksize`, `conv_num_outputs` and the shape of `x_tensor`.
# * Apply a convolution to `x_tensor` using weight and `conv_strides`.
#  * We recommend you use same padding, but you're welcome to use any padding.
# * Add bias
# * Add a nonlinear activation to the convolution.
# * Apply Max Pooling using `pool_ksize` and `pool_strides`.
#  * We recommend you use same padding, but you're welcome to use any padding.
# 
# **Note:** You **can't** use [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) for **this** layer, but you can still use TensorFlow's [Neural Network](https://www.tensorflow.org/api_docs/python/tf/nn) package. You may still use the shortcut option for all the **other** layers.

Ejemplo n.º 2

Archivo: dlnd_image_classification.py Proyecto: Hushengjing/Deep-Learning-Project

def neural_net_keep_prob_input():
    """
    Return a Tensor for keep probability
    : return: Tensor for keep probability.
    """
    # TODO: Implement Function
    prob = tf.placeholder(tf.float32, name='keep_prob')
    return prob


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tf.reset_default_graph()
tests.test_nn_image_inputs(neural_net_image_input)
tests.test_nn_label_inputs(neural_net_label_input)
tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)


# ### 卷积和最大池化层
# 
# 卷积层级适合处理图片。对于此代码单元，你应该实现函数 `conv2d_maxpool` 以便应用卷积然后进行最大池化：
# 
# * 使用 `conv_ksize`、`conv_num_outputs` 和 `x_tensor` 的形状创建权重（weight）和偏置（bias）。
# * 使用权重和 `conv_strides` 对 `x_tensor` 应用卷积。
#  * 建议使用我们建议的间距（padding），当然也可以使用任何其他间距。
# * 添加偏置
# * 向卷积中添加非线性激活（nonlinear activation）
# * 使用 `pool_ksize` 和 `pool_strides` 应用最大池化
#  * 建议使用我们建议的间距（padding），当然也可以使用任何其他间距。