def test_implementation():
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)
tests.test_con_pool(conv2d_maxpool)
tests.test_flatten(flatten)
tests.test_fully_conn(fully_conn)
tests.test_output(output)
build_cnn()
tests.test_conv_net(conv_net)
tests.test_train_nn(train_neural_network)
def run_tests():
import problem_unittests as t
t.test_folder_path(cifar10_dataset_folder_path)
t.test_normalize(normalize)
t.test_one_hot_encode(one_hot_encode)
t.test_nn_image_inputs(neural_net_image_input)
t.test_nn_label_inputs(neural_net_label_input)
t.test_nn_keep_prob_inputs(neural_net_keep_prob_input)
t.test_con_pool(conv2conv2d_maxpool)
t.test_flatten(flatten)
t.test_fully_conn(fully_conn)
t.test_output(output)
t.test_conv_net(conv_net)
t.test_train_nn(train_neural_network)
"""
Return a Tensor for keep probability
: return: Tensor for keep probability.
"""
# TODO: Implement Function
keep_prob = tf.placeholder(tf.float32, name = "keep_prob")
return 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.
# In[ ]:
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.
def runLabelInputTests(self):
tests.test_nn_label_inputs(neural_net_label_input)
print("Model LabelInputTests Ran Successfully")
