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)
"""
Apply a fully connected layer to x_tensor using weight and bias
: x_tensor: A 2-D tensor where the first dimension is batch size.
: num_outputs: The number of output that the new tensor should be.
: return: A 2-D tensor where the second dimension is num_outputs.
"""
# TODO: Implement Function
fc_layer = tf.layers.dense(x_tensor, num_outputs, activation=tf.nn.relu)
return fc_layer
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_fully_conn(fully_conn)
# ### Output Layer
# Implement the `output` function to apply a fully connected layer to `x_tensor` with the shape (*Batch Size*, *num_outputs*).
#
# Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages for this layer. For more of a challenge, only use other TensorFlow packages.
#
# **Note:** Activation, softmax, or cross entropy should **not** be applied to this.
# In[ ]:
def output(x_tensor, num_outputs):
"""
# TODO: Implement Function
input_size = x_tensor.get_shape().as_list()[1]
output_size = num_outputs
weights = tf.Variable(tf.truncated_normal([input_size, output_size], mean = 0.0, stddev = 1.0 / input_size))
biases = tf.Variable(tf.zeros(num_outputs))
fc_out = tf.add(tf.matmul(x_tensor, weights), biases)
fc_out = tf.nn.relu(fc_out)
return fc_out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_fully_conn(fully_conn)
# ### Output Layer
# Implement the `output` function to apply a fully connected layer to `x_tensor` with the shape (*Batch Size*, *num_outputs*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages for this layer. For more of a challenge, only use other TensorFlow packages.
#
# **Note:** Activation, softmax, or cross entropy should **not** be applied to this.
# In[11]:
def output(x_tensor, num_outputs):
"""
Apply a output layer to x_tensor using weight and bias
: x_tensor: A 2-D tensor where the first dimension is batch size.
: num_outputs: The number of output that the new tensor should be.
def runFullyConvLayerTests(self):
tests.test_fully_conn(fully_conn)
print("Model FullyConvLayerTests Ran Successfully")