def runOutputLayerTests(self):
tests.test_output(output)
##############################
## Build the Neural Network ##
##############################
# Remove previous weights, bias, inputs, etc..
tests.test_conv_net(conv_net)
print("Model OutputLayerTests Ran Successfully")
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 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.
: return: A 2-D tensor where the second dimension is num_outputs.
"""
# TODO: Implement Function
output_layer = tf.layers.dense(x_tensor, num_outputs, activation=None)
return output_layer
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_output(output)
# ### Create Convolutional Model
# Implement the function `conv_net` to create a convolutional neural network model. The function takes in a batch of images, `x`, and outputs logits. Use the layers you created above to create this model:
#
# * Apply 1, 2, or 3 Convolution and Max Pool layers
# * Apply a Flatten Layer
# * Apply 1, 2, or 3 Fully Connected Layers
# * Apply an Output Layer
# * Return the output
# * Apply [TensorFlow's Dropout](https://www.tensorflow.org/api_docs/python/tf/nn/dropout) to one or more layers in the model using `keep_prob`.
# In[ ]:
: return: A 2-D tensor where the second dimension is 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))
out = tf.add(tf.matmul(x_tensor, weights), biases)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_output(output)
# ### Create Convolutional Model
# Implement the function `conv_net` to create a convolutional neural network model. The function takes in a batch of images, `x`, and outputs logits. Use the layers you created above to create this model:
#
# * Apply 1, 2, or 3 Convolution and Max Pool layers
# * Apply a Flatten Layer
# * Apply 1, 2, or 3 Fully Connected Layers
# * Apply an Output Layer
# * Return the output
# * Apply [TensorFlow's Dropout](https://www.tensorflow.org/api_docs/python/tf/nn/dropout) to one or more layers in the model using `keep_prob`.
# In[12]:
