eInvoiceMeis in the Finance-Accounting Tech Industry

eInvoiceMe

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Further analysis This study's findings diverge from those of Gregorcic and Pendrill (2023), who contended that despite rectifying errors and contradictions via Socratic dialogue, ChatGPT did not meet the necessary criteria to function as a physics tutor. This disparity could stem from using an older, less advanced iteration of ChatGPT, specifically the GPT-3 model introduced by OpenAI in 2020 (Brown et al., 2020). Our research indicates that the current versions of ChatGPT, Bing Chat, Bard, and Claude can act as effective objects-to-think-with, as per Papert's concept (1980, p. 11), cultivating an engaging, interactive, and inclusive learning environment. These AI tools can support creativity, collaboration, exploration, critical thinking, and problem-solving, potentially deepening understanding of the subject matter. This relates to the comparison between ChatGPT, Bard, and Bing Chat in the study of Liu et al. (2023). The overall success of this educational journey, with GenAIbots serving as agents to think with, highlights a few effective teaching strategies that can facilitate a profound comprehension of the involved concepts. A significant part of the process involved positive reinforcement, as GenAIbots acknowledged students' observations as enjoyable, validating their experiences and stimulating their curiosity. Furthermore, following Gregorcic and Pendrill (2023), we adopted what could be seen as a Socratic-like dialogue strategy, prompting students to think critically and actively engage in their learning rather than merely providing them with answers. GenAIbots also capitalised on the student’s existing knowledge by inquiring about their understanding of topics like weight and gravity, thereby establishing a meaningful bridge between new and previously learned information. Building upon Bitzenbauer's preliminary study (2023) that highlighted the potential of expansive language model-based chatbots, including ChatGPT, to enrich learning experiences, and Adiguzel et al.'s (2023) assertion of ChatGPT's transformative potential in education via personalised instruction and real-time feedback, our research emphasises the benefits of AI integration in learning environments. AI-driven chatbots like ChatGPT, Bing Chat, Bard, Claude, and other generative models might serve as advanced agents for addressing challenging learning scenarios, expanding students' knowledge incrementally, and providing relevant explanations, examples, and analogies to support their learning. This study highlights the potential of AI-driven tools in promoting active learning, personalised instruction, and conceptual understanding in Chemistry. It underscores the value of AI in enhancing educational experiences, concluding that GenAIbots can serve as innovative platforms for exploring challenging educational scenarios. Nevertheless, our analysis reveals that ChatGPT and Claude consistently provided more comprehensive, detailed, and accurate answers than Bing Chat, which tended to give shorter and less informative responses. ChatGPT-4 was able to adapt responses to students' questions, address misconceptions, and offer customised explanations. ChatGPT was also more adept at addressing the nuances and subtleties of the questions, recognising the context, and engaging with the user's unique perspective or interpretation. On the other hand, Bing Chat often failed to address these nuances and provided less contextual information. Overall, ChatGPT and Claude demonstrated a superior understanding of the subject matter and a more remarkable ability to convey complex scientific concepts in an accessible manner. The study also highlighted the need for comprehensive educator training before integrating these tools into classrooms. Adequate training empowers teachers to guide students effectively in articulating their thoughts and constructing solid arguments. This preparation would allow educators to assess GenAIbots' performance when faced with compelling counterexamples, fostering productive discussions among colleagues. Finally, it is essential to mention that the results of this experience revealed significant variance in the performances of ChatGPT, Bing Chat, Bard, and Claude. This result agrees with Franciscu (2023), in which ChatGPT, a cutting-edge natural language generation model, excels at producing human-like responses suitable for chatbots. Based on the detailed analysis above, despite some fundamental similarities, ChatGPT notably outperformed BingChat, with Bard and Claude closely shadowing ChatGPT's performance and displaying comparable proficiency levels between them, as described below and summarised in Table 2. 1. stands out for its consistent through positive reinforcement and feedback mechanisms. It employs effective , often breaking down explanations systematically, and frequently engages users with by soliciting feedback. With a flair for the , translates abstract concepts into relatable scenarios. Its is tailored and comprehensive, and it occasionally ventures into to provide broader contexts. ChatGPT Personalization & Personable Touch Instructional Strategies Engagement & Interactivity Use of Analogy & Comparative Illustrations ChatGPT Detail Depth & Content Recommendation Meta-discussion & Perspective Framing 2. strengths lie predominantly in and , making learning feel like an interactive dialogue. Offering straightforward explanations, it leans heavily on the especially when building upon user insights. While its provide clarity, the depth can vary, catering to concise and elaborate queries. penchant for emphasises the broader significance of concepts, enhancing the overall learning experience. Claude's Personalization & Personable Touch Engagement & Interactivity Use of Analogy & Comparative Illustrations, Instructional Strategies Claude's Meta-discussion & Perspective Framing 3. responses resemble a classic approach, akin to a conventional classroom style. Focusing on factual and structured presentations, it often employs the to elucidate concepts. is evident, especially when recognising user inputs and providing deeper insights, fostering Engagement & Interactivity. Bard occasionally integrates to highlight broader applications. Bard's Instructional Strategies Use of Analogy & Comparative Illustrations Personalization & Personable Touch Meta-discussion & Perspective Framing 4. emphasises by often directing users to external resources. Adopting a more correctional R approach, it points out inaccuracies in user inputs, which can sometimes overshadow its While provides structured , its reliance on external links might diminish direct Engagement & Interactivity. Its is present, albeit less pronounced. BingChat Detail Depth & Content Recommendation einforcement & Feedback Personalization & Personable Touch. BingChat Instructional Strategies Use of Analogy & Comparative Illustrations This ranking is based on the analysis presented earlier. It provides a comparative grade for each AI across different categories, but the specific grading might be subjective and could vary with different questions, contexts or even across sessions. The preference might also be subjective depending on individual learning styles and needs. Using GenAIbots as educational tools, teachers might create more engaging and personalised learning experiences. This approach could potentially support a deeper understanding of complex concepts in Chemistry and perhaps other subjects. This paper is under CC BY-SA 4.0 DEED license. available on arxiv

Analyzing the Role of AI Chatbots in Active and Personalized Education

সক্রিয় এবং ব্যক্তিগতকৃত শিক্ষায় এআই চ্যাটবটগুলির ভূমিকা বিশ্লেষণ করা

Analyse der Rolle von KI-Chatbots in aktiver und personalisierter Bildung

Análisis del papel de los chatbots de IA en la educación activa y personalizada

Analyser le rôle des chatbots IA dans l'éducation active et personnalisée

सक्रिय और व्यक्तिगत शिक्षा में एआई चैटबॉट्स की भूमिका का विश्लेषण

アクティブでパーソナライズされた教育における AI チャットボットの役割の分析

능동적이고 개인화된 교육에서 AI 챗봇의 역할 분석

Analisando o papel dos chatbots de IA na educação ativa e personalizada

Анализ роли чат-ботов с искусственным интеллектом в активном и персонализированном образовании

Aktif ve Kişiselleştirilmiş Eğitimde Yapay Zeka Chatbotlarının Rolünün Analizi

Phân tích vai trò của Chatbot AI trong giáo dục tích cực và cá nhân hóa

分析人工智能聊天机器人在主动和个性化教育中的作用

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Question about changing of state *: You mentioned water and water vapor. this is what my professor calls a change of state, right?* P1 *: Yes, exactly! A change of state, also known as a phase transition, refers to the transformation of a substance from one state of matter to another. The most common states of matter are solid, liquid, and gas. The transitions between these states are typically due to changes in temperature and/or pressure.* ChatGPT Let's go over the primary changes of state: 1. Melting: The transition from solid to liquid. For instance, when ice (solid water) warms up, it melts to form liquid water. *: The transition from liquid to solid. It's the opposite of melting. When liquid water cools down, it freezes to form ice.* 2. Freezing 3. Boiling (or Vaporization): The transition from liquid to gas. When water is heated to its boiling point, it turns into water vapor (steam). 4. Condensation: The transition from gas to liquid. It's the opposite of boiling. When steam cools down, it condenses to form liquid water. 5. Sublimation: The transition directly from solid to gas without passing through the liquid state. Dry ice (solid carbon dioxide) does this—it turns directly into carbon dioxide gas when it warms up. 6. Deposition: The transition directly from gas to solid without passing through the liquid state. It's the opposite of sublimation. Each substance has specific temperatures at which these changes of state occur, called its melting point, freezing point, boiling point, etc. These points can also vary based on external pressure. Does this explanation help clarify the concept of changes of state for you? Would you like to dive deeper into any specific transition or related topic? BingChat: Yes, you are right. Water and water vapour are examples of a change of state. A change of state is a physical change in which a substance changes from one state of matter to another. The three common states of matter are solid, liquid, and gas. Water can exist in all three states, depending on the temperature and pressure. For example, water is a liquid at room temperature and normal atmospheric pressure, but it can change to a solid (ice) when it is cooled below 0°C, or to a gas (water vapour) when it is heated above 100°C[ ] 123 . There are different names for the processes that cause a change of state. For example, when a liquid changes to a gas, it is called vaporization. Vaporization can happen in two ways: evaporation or boiling. Evaporation is when a liquid changes to a gas at the surface, without reaching its boiling point. Boiling is when a liquid changes to a gas throughout the liquid, when it reaches its boiling point. When a gas changes to a liquid, it is called condensation. Condensation is when a gas loses heat and turns into a liquid droplet[ ] 123 A change of state does not affect the chemical composition of a substance. That means the substance does not change into a new substance with different properties. For example, water and water vapour are both made of the same molecules (H2O), but they have different physical properties such as shape, volume, and density. A change of state can be reversed by changing the temperature or pressure again. For example, water vapour can change back to liquid water by cooling it down[ ]. 123 I hope this helps you understand what a change of state is and how it happens. If you want to learn more about the changes of state of water and other substances, you can watch this video or read this article. *: Yes, that is correct. Water and water vapor are two different states of water. Water is a liquid at room temperature, but it can be converted to a gas (water vapor) by heating it.* Bard Conversely, water vapor can be converted to a liquid (water) by cooling it. This process of changing the state of a substance is called a change of state. There are three main states of matter: solid, liquid, and gas. Each state has different properties. For example, solids have a definite shape and volume. Liquids have a definite volume, but they take the shape of their container. Gases have neither a definite shape nor a definite volume. When a substance changes state, it absorbs or releases energy. For example, when water vapor condenses to form liquid water, it releases energy. This energy is what causes the air around us to feel warm when it rains. Changes of state are important in many different natural processes, such as the water cycle and weather formation. They are also used in many different industrial processes, such as food processing and medicine. I hope this helps to explain what a change of state is. Please let me know if you have any other questions. *: Yes, that's correct! When water transitions between liquid water and water vapor, it is undergoing a change of state.* Claude The different states of matter are: • Solid - Fixed shape and volume, tightly packed molecules • Liquid - Fixed volume but variable shape, flowing molecules • Gas - Variable volume and shape, molecules move freely Water can exist in all three states: • Ice (solid) • Liquid water • Water vapor (gas) Some key changes of state for water: • Melting - Solid to liquid, ice melts to water • Freezing - Liquid to solid, water freezes to ice • Evaporation - Liquid to gas, water evaporates to vapor • Condensation - Gas to liquid, vapor condenses into water The processes of evaporation and condensation describe water transitioning between the liquid and gas states. This is what produces water vapor. Other examples of changes of state include: • Boiling - Liquid to gas, water boils to steam • Sublimation - Solid to gas, dry ice (solid CO2) sublimates to CO2 gas Let me know if you need any clarification on changes of state or the different phases of matter! Being comfortable with these concepts is important for understanding many chemical processes. acknowledges the student’s understanding with an “Yes, exactly!,” offering . It then directly tackles the student's question about water and water vapor, providing a thorough and structured breakdown of state changes, leaning to and saying, “Let's go over”. It finishes the explanation recurring to Metadiscussion & Perspective Framing referring that "Each substance has specific temperatures." It concludes with "Does this explanation help clarify" "for you?" offering R and , respectively, and adeptly inviting further exploration with "Would you like to dive deeper", what points to Detail Depth & Content Recommendation. similarly recognizes the student's comprehension by stating "Yes, you are right," providing . It not only offers a clear and presentation including examples, what hit the and Use of Analogy & Comparative Illustrations categories, but also integrates external content recommendations, pointing to . It also adds that "A change of state can be reversed," offering . It finalizes with “I hope this helps”, and a smiley, signs of and Personalization & Personable Touch respectively and “If you want to learn more” “you can watch this video or read this article” pointing to . Bard likewise recognizes the student's grasp by stating, "Yes, that is correct," exhibiting a . It then provides a concise yet illuminating on the implications of state changes being "important in many different natural processes," "water cycle," and "weather formation" and "in many different industrial processes," "food processing" and "medicine", connecting the topic to broader contexts. The explanation concludes with "I hope this helps to explain," showcasing , followed by "Please let me know if you have any other questions," which emphasizes . Like the earlier GenAIbots, Claude begins by recognizing the student's comprehension with “Yes, that's correct!,” adding . It then presents the states of matter and their transitions with clear analogies, leaning to , and extending the explanation with "other examples of changes of state", such as "Boiling" and "Sublimation," what hit the , although its depth might be a tad more concise that the previous ones. It then concludes with “Let me know if you need any clarification,” which emphasizes and includes the remark, “Being comfortable with these concepts is important for understanding many chemical processes,” aligning with the category. ChatGPT Personalization & Personable Touch Instructional Strategies einforcement & Feedback Personalization & Personable Touch BingChat Personalization & Personable Touch Detail Depth & Content Recommendation Depth & Content Recommendation Meta-discussion & Perspective Framing Engagement & Interactivity Detail Depth & Content Recommendation Personalization & Personable Touch Meta-discussion & Perspective Framing Engagement & Interactivity Personalization & Personable Touch Personalization & Personable Touch Instructional Strategies Detail Depth & Content Recommendation Personalization & Personable Touch Meta-discussion & Perspective Framing This paper is under CC BY-SA 4.0 DEED license. available on arxiv

Mastering Basic Chemistry with GenAIbots: Exploring Changes of State with Water and Water Vapor

The fast and easy guide to the most popular Deep Learning framework in the world. Make sure to check out the other articles . here Overview In this installment we will be going over all the abstracted models that are currently available in TensorFlow and describe use cases for that particular model as well as simple sample code. Full sources of working examples are in the . TensorFlow In a Nutshell repo A recurrent neural network Recurrent Neural Networks Language Modeling, Machine translation, Word embedding, Text processing. Use Cases: Since the advent of Long Short Term Memory and Gated Recurrent Units, Recurrent Neural Networks have made leaps and bounds above other models in natural language processing. They can be fed vectors representing characters and be trained to generate new sentences based on the training set. The merit in this model is that it keeps the context of the sentence and derives meaning that “cat sat on the mat” means the cat is on the mat. Since the creation of TensorFlow writing these networks have become increasingly simpler. There are even hidden features covered by Denny Britz that make writing RNN’s even simpler heres a quick example. here import tensorflow as tfimport numpy as np X = np.random.randn(2, 10, 8) # Create input data X[1,6,:] = 0X_lengths = [10, 6] # The second example is of length 6 cell = tf.nn.rnn_cell.LSTMCell(num_units=64, state_is_tuple= )cell = tf.nn.rnn_cell.DropoutWrapper(cell=cell, output_keep_prob=0.5)cell = tf.nn.rnn_cell.MultiRNNCell(cells=[cell] * 4, state_is_tuple= ) True True outputs, last_states = tf.nn.dynamic_rnn(cell=cell,dtype=tf.float64,sequence_length=X_lengths,inputs=X) result = tf.contrib.learn.run_n({"outputs": outputs, "last_states": last_states},n=1,feed_dict= ) None Convolution Neural Network Convolution Neural Networks Image processing, Facial recognition, Computer Vision Use Cases: Convolution Neural Networks are unique because they’re created in mind that the input will be an image. CNNs perform a sliding window function to a matrix. The window is called a kernel and it slides across the image creating a convolved feature. from http://deeplearning.standford.edu/wiki/index.php/Feature_extraction_using_convolution Creating a convolved feature allows for edge detection which then allows for a to depict objects from pictures. network edge detection from GIMP manual The convolved feature to create this looks like this matrix below: Convolved feature from GIMP manual Here’s a sample of code to identify handwritten digits from the MNIST dataset. ### Convolutional networkdef max_pool_2x2(tensor_in):return tf.nn.max_pool(tensor_in, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') def conv_model(X, y): reshape X to 4d tensor with 2nd and 3rd dimensions being image width and height final dimension being the number of color channels. X = tf.reshape(X, [-1, 28, 28, 1]) first conv layer will compute 32 features for each 5x5 patch with tf.variable_scope('conv_layer1'):h_conv1 = learn.ops.conv2d(X, n_filters=32, filter_shape=[5, 5],bias=True, activation=tf.nn.relu)h_pool1 = max_pool_2x2(h_conv1) second conv layer will compute 64 features for each 5x5 patch. with tf.variable_scope('conv_layer2'):h_conv2 = learn.ops.conv2d(h_pool1, n_filters=64, filter_shape=[5, 5],bias=True, activation=tf.nn.relu)h_pool2 = max_pool_2x2(h_conv2) # reshape tensor into a batch of vectors  
h\_pool2\_flat = tf.reshape(h\_pool2, \[-1, 7 \* 7 \* 64\]) densely connected layer with 1024 neurons. h_fc1 = learn.ops.dnn(h_pool2_flat, [1024], activation=tf.nn.relu, dropout=0.5)return learn.models.logistic_regression(h_fc1, y) Feed Forward Neural Networks Classification and Regression Use Cases: These networks consist of perceptrons in layers that take inputs that pass information on to the next layer. The last layer in the network produces the output. There is no connection between each node in a given layer. The layer that has no original input and no final output is called the hidden layer. The goal of this network is similar to other supervised neural networks using back propagation, to make inputs have the desired trained outputs. These are some of the simplest effective neural networks for classification and regression problems. We will show how easy it is to create a feed forward network to classify handwritten digits: def init_weights(shape):return tf.Variable(tf.random_normal(shape, stddev=0.01)) def model(X, w_h, w_o):h = tf.nn.sigmoid(tf.matmul(X, w_h)) # this is a basic mlp, think 2 stacked logistic regressionsreturn tf.matmul(h, w_o) # note that we dont take the softmax at the end because our cost fn does that for us mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels X = tf.placeholder("float", [None, 784])Y = tf.placeholder("float", [None, 10]) w_h = init_weights([784, 625]) # create symbolic variablesw_o = init_weights([625, 10]) py_x = model(X, w_h, w_o) cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y)) # compute coststrain_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost) # construct an optimizerpredict_op = tf.argmax(py_x, 1) # Launch the graph in a sessionwith tf.Session() as sess:# you need to initialize all variablestf.initialize_all_variables().run() for i in range(100):for start, end in zip(range(0, len(trX), 128), range(128, len(trX)+1, 128)):sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})print(i, np.mean(np.argmax(teY, axis=1) ==sess.run(predict_op, feed_dict={X: teX, Y: teY}))) Linear Models Classification and Regression Use Cases: Linear models take X values and produce a line of best fit used for classification and regression of Y values. For example if you have a list of house sizes and their price in a neighborhood you can predict the price of house given the size using a linear model. One thing to note is that linear models can be used for multiple X features. For example in the housing example we can create a linear model given house sizes, how many rooms, how many bathrooms and price and predict price given a house with size, # of rooms, # of bathrooms. import numpy as npimport tensorflow as tf def weight_variable(shape):initial = tf.truncated_normal(shape, stddev=1)return tf.Variable(initial) # datasetxx = np.random.randint(0,1000,[1000,3])/1000.yy = xx[:,0] * 2 + xx[:,1] * 1.4 + xx[:,2] * 3 # modelx = tf.placeholder(tf.float32, shape=[None, 3])y_ = tf.placeholder(tf.float32, shape=[None])W1 = weight_variable([3, 1])y = tf.matmul(x, W1) # training and cost functioncost_function = tf.reduce_mean(tf.square(tf.squeeze(y) - y_))train_function = tf.train.AdamOptimizer(1e-2).minimize(cost_function) # create a sessionsess = tf.Session() # trainsess.run(tf.initialize_all_variables())for i in range(10000):sess.run(train_function, feed_dict={x:xx, y_:yy})if i % 1000 == 0:print(sess.run(cost_function, feed_dict={x:xx, y_:yy})) Support Vector Machines Currently only Binary Classification Use Cases: The general idea behind a SVM is that there is an optimal hyperplane for linearly separable patterns. For data that is not linearly separable we can use a kernel function to transform the original data into a new space. SVMs maximize the margin around separating the hyperplane. They work extremely well in high dimensional spaces and and are still effective if the dimensions are greater than the number of samples. def input_fn():return {'example_id': tf.constant(['1', '2', '3']),'price': tf.constant([[0.6], [0.8], [0.3]]),'sq_footage': tf.constant([[900.0], [700.0], [600.0]]),'country': tf.SparseTensor(values=['IT', 'US', 'GB'],indices=[[0, 0], [1, 3], [2, 1]],shape=[3, 5]),'weights': tf.constant([[3.0], [1.0], [1.0]])}, tf.constant([[1], [0], [1]]) price = tf.contrib.layers.real_valued_column('price')sq_footage_bucket = tf.contrib.layers.bucketized_column(tf.contrib.layers.real_valued_column('sq_footage'),boundaries=[650.0, 800.0])country = tf.contrib.layers.sparse_column_with_hash_bucket('country', hash_bucket_size=5)sq_footage_country = tf.contrib.layers.crossed_column([sq_footage_bucket, country], hash_bucket_size=10)svm_classifier = tf.contrib.learn.SVM(feature_columns=[price, sq_footage_bucket, country, sq_footage_country],example_id_column='example_id',weight_column_name='weights',l1_regularization=0.1,l2_regularization=1.0) svm_classifier.fit(input_fn=input_fn, steps=30)accuracy = svm_classifier.evaluate(input_fn=input_fn, steps=1)['accuracy'] Deep and Wide Models Recommendation systems, Classification and Regression Use Cases: Deep and Wide models were covered with greater detail in , so we won’t get too heavy here. A Wide and Deep Network combines a linear model with a feed forward neural net so that our predictions will have memorization and generalization. This type of model can be used for classification and regression problems. This allows for less feature engineering with relatively accurate predictions. Thus, getting the best of both worlds. Here’s a code snippet from . part two part two’s github def input_fn(df, train=False):"""Input builder function.""" Creates a dictionary mapping from each continuous feature column name (k) to the values of that column stored in a constant Tensor. continuous_cols = {k: tf.constant(df[k].values) for k in CONTINUOUS_COLUMNS} Creates a dictionary mapping from each categorical feature column name (k) to the values of that column stored in a tf.SparseTensor. categorical_cols = {k: tf.SparseTensor(indices=[[i, 0] for i in range(df[k].size)],values=df[k].values,shape=[df[k].size, 1])for k in CATEGORICAL_COLUMNS} Merges the two dictionaries into one. feature_cols = dict(continuous_cols)feature_cols.update(categorical_cols) Converts the label column into a constant Tensor. if train:label = tf.constant(df[SURVIVED_COLUMN].values)# Returns the feature columns and the label.return feature_cols, labelelse:return feature_cols m = build_estimator(model_dir)m.fit(input_fn=lambda: input_fn(df_train, True), steps=200)print m.predict(input_fn=lambda: input_fn(df_test))results = m.evaluate(input_fn=lambda: input_fn(df_train, True), steps=1)for key in sorted(results):print("%s: %s" % (key, results[key])) Random Forest Classification and Regression Use Cases: Random Forest model takes many different classification trees and each tree votes for that class. The forest chooses the classification having the most votes. Random Forests do not overfit, you can run as many treees as you want and it is relatively fast. Give it a try on the iris data with this snippet below: hparams = tf.contrib.tensor_forest.python.tensor_forest.ForestHParams(num_trees=3, max_nodes=1000, num_classes=3, num_features=4)classifier = tf.contrib.learn.TensorForestEstimator(hparams) iris = tf.contrib.learn.datasets.load_iris()data = iris.data.astype(np.float32)target = iris.target.astype(np.float32) monitors = [tf.contrib.learn.TensorForestLossMonitor(10, 10)]classifier.fit(x=data, y=target, steps=100, monitors=monitors)classifier.evaluate(x=data, y=target, steps=10) Bayesian Reinforcement Learning Classification and Regression Use Cases: In the contrib folder of TensorFlow there is a library called BayesFlow. BayesFlow has no documentation except for an example of the REINFORCE algorithm. This algorithm is proposed in a by Ronald Williams. paper ward ncrement = onnegative actor * ffset einforcement * haracteristic ligibility RE I N F O R C E This network trying to solve an immediate reinforcement task, adjusts the weights after getting the reinforcement value at each trial. At the end of each trial each weight is incremented by a learning rate factor multiplied by the reinforcement value minus the baseline multiplied by characteristic eligibility. Williams paper also discusses the use of back propagation to train the REINFORCE network. learning """Build the Split-Apply-Merge Model.Route each value of input [-1, -1, 1, 1] through one of thefunctions, plus_1, minus_1.  The decision for routing is made by4 Bernoulli R.V.s whose parameters are determined by a neural networkapplied to the input.  REINFORCE is used to update the NN parameters.Returns:The 3-tuple (route_selection, routing_loss, final_loss), where:- route_selection is an int 4-vector- routing_loss is a float 4-vector- final_loss is a float scalar."""inputs = tf.constant([[-1.0], [-1.0], [1.0], [1.0]])targets = tf.constant([[0.0], [0.0], [0.0], [0.0]])paths = [plus_1, minus_1]weights = tf.get_variable("w", [1, 2])bias = tf.get_variable("b", [1, 1])logits = tf.matmul(inputs, weights) + bias # REINFORCE forward steproute_selection = st.StochasticTensor(distributions.Categorical, logits=logits) Linear Chain Conditional Random Fields Sequential Data Use Cases: CRFs are conditional probability distributions that factoirze according to an undirected model. They predict a label for a single sample keeping context from the neighboring samples. CRFs are similar to Hidden Markov Models. CRFs are often used for image segmentation and object recognition, as well as shallow parsing, named entity recognition and gene finding. # Train for a fixed number of iterations.session.run(tf.initialize_all_variables())for i in range(1000):tf_unary_scores, tf_transition_params, _ = session.run([unary_scores, transition_params, train_op])if i % 100 == 0:correct_labels = 0total_labels = 0for tf_unary_scores_, y_, sequence_length_ in zip(tf_unary_scores, y, sequence_lengths):# Remove padding from the scores and tag sequence.tf_unary_scores_ = tf_unary_scores_[:sequence_length_]y_ = y_[:sequence_length_] # Compute the highest scoring sequence.  
    viterbi\_sequence, \_ = tf.contrib.crf.viterbi\_decode(  
        tf\_unary\_scores\_, tf\_transition\_params)  

    # Evaluate word-level accuracy.  
    correct\_labels += np.sum(np.equal(viterbi\_sequence, y\_))  
    total\_labels += sequence\_length\_  
  accuracy = 100.0 \* correct\_labels / float(total\_labels)  
  print("Accuracy: %.2f%%" % accuracy) Conclusion Ever since TensorFlow has been released the community surrounding the project has been adding more packages, examples and cases for using this amazing library. Even at the time of writing this article there are more models and sample code being written. It is amazing to see how much TensorFlow as grown in these past few months. The ease of use and diversity in the package are increasing overtime and don’t seem to be slowing down anytime soon. As always — Feel free to email me any questions or inquiries at camron@camron.xyz Originally posted at Camron.xyz

TensorFlow in a Nutshell — Part Three: All the Models

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