# Puzzle Solution Revealed - Transformer: Need for Position Embedding [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg align="center")](https://colab.research.google.com/github/techaarvam/byom_workshop/blob/main/attention_ann_part2_puzzle_solution.ipynb) # Introduce position embedding, residuals and layers [![Introduce position embedding, residuals and layers - watch the walkthrough](https://i.ytimg.com/vi/Hav2zlqxzUI/maxresdefault.jpg align="center")](https://youtu.be/Hav2zlqxzUI) ▶ [**Watch the 7-minute walkthrough**](https://youtu.be/Hav2zlqxzUI) ```python import torch torch.set_printoptions(precision=2, sci_mode=False, linewidth=160) ``` In this notebook we are extending the previous notebook, [attention\_ann.ipynb](https://colab.research.google.com/github/techaarvam/byom_workshop/blob/main/attention_ann.ipynb), which introduced (plausibly) the world's tiniest hand-constructed transformer model. Please read the previous notebook for the context. This is the solution to the puzzle that was introduced as part of the previous notebook. We add one word 'disobeys' which modifies the action-attribute words (swap-speech, keep-speech, swap-flight, keep-flight). In the previous notebook the order of the words did not matter. In the current notebook, the order does matter. The word disobeys modifies only the word right after it, so word order matters. The solution adds an extra layer, and a residual connection that carries the input unmodified to the next block, so each next layer gets both the unmodified input and the modified input. The residual stream is made 22 bits, where it carries the original inputs and each layer's/head's findings. The most interesting addition is the position information. Each token carries the position where it appears. This example is constructed to illustrate the ideas. In the real implementation, we do not hand-construct in this manner. Vector embedding and position embedding are often also learned using the training loop and gradient descent. But the hand-construction allows us to see why those blocks and connections exist and how they are helpful to have. ### Residual layout | idx | slot | | --- | --- | | 0 | `fly` | | 1 | `speak` | | 2 | `swap_fly` | | 3 | `swap_speak` | | 4 | `object` | | 5 | `action_fly` | | 6 | `action_speak` | | 7 | `question` | | 8 | `disobey` | | 9 | `pos0` | | 10 | `pos1` | | 11 | `pos2` | | 12 | `pos3` | | 13 | `pos4` | | 14 | `pos5` | | 15 | `previous_word_is_disobey` | | 16 | `object_attribute_fly` | | 17 | `object_attribute_speak` | | 18 | `is_swap_attr_fly` | | 19 | `is_attr_fly_disobeyed` | | 20 | `is_swap_attr_speak` | | 21 | `is_attr_speak_disobeyed` | ```python # The new list of token bits, with the additional word 'disobeys' idx = {"fly": 0, "speak": 1, "swap_fly": 2, "swap_speak": 3, "object": 4, "action_fly": 5, "action_speak": 6, "question": 7, "disobey": 8} # constants used for bit-slicing and locating the portion of # the residual we need # L is the maximum sentence length, i.e. the number of position slots. # It is used to name the position slots below, and to build the # "Disobey Position Finder" head (Wq_P, Wk_P) in layer 1. position_start, L = 9, 6 # position one-hot occupies 9 .. 14 previous_word_is_disobey = 15 object_attribute_fly, object_attribute_speak = 16, 17 is_swap_attr_fly, is_attr_fly_disobeyed = 18, 19 is_swap_attr_speak, is_attr_speak_disobeyed = 20, 21 num_bits = 22 slot_names = {v: k for k, v in idx.items()} slot_names.update({position_start + p: f"pos{p}" for p in range(L)}) slot_names.update({previous_word_is_disobey: "previous_word_is_disobey", object_attribute_fly: "object_attribute_fly", object_attribute_speak: "object_attribute_speak", is_swap_attr_fly: "is_swap_attr_fly", is_attr_fly_disobeyed: "is_attr_fly_disobeyed", is_swap_attr_speak: "is_swap_attr_speak", is_attr_speak_disobeyed: "is_attr_speak_disobeyed"}) for i in range(num_bits): print(f"{i:2} {slot_names[i]}") ``` ```plaintext 0 fly 1 speak 2 swap_fly 3 swap_speak 4 object 5 action_fly 6 action_speak 7 question 8 disobey 9 pos0 10 pos1 11 pos2 12 pos3 13 pos4 14 pos5 15 previous_word_is_disobey 16 object_attribute_fly 17 object_attribute_speak 18 is_swap_attr_fly 19 is_attr_fly_disobeyed 20 is_swap_attr_speak 21 is_attr_speak_disobeyed ``` ### Vocabulary ```python token_to_vector = { # fly spk swF swS obj actF actS q dis "Rock": [0, 0, 0, 0, 1, 0, 0, 0, 0], "Human": [0, 1, 0, 0, 1, 0, 0, 0, 0], "Crow": [1, 0, 0, 0, 1, 0, 0, 0, 0], "Flying superhero": [1, 1, 0, 0, 1, 0, 0, 0, 0], "swap-flight": [0, 0, 1, 0, 0, 1, 0, 0, 0], "swap-speech": [0, 0, 0, 1, 0, 0, 1, 0, 0], "keep-flight": [0, 0, 0, 0, 0, 1, 0, 0, 0], "keep-speech": [0, 0, 0, 0, 0, 0, 1, 0, 0], "disobeys": [0, 0, 0, 0, 0, 0, 0, 0, 1], "he-is?": [0, 0, 0, 0, 0, 0, 0, 1, 0], } for tok, bits in token_to_vector.items(): print(f"{tok:18} {torch.tensor(bits)}") ``` ```plaintext Rock tensor([0, 0, 0, 0, 1, 0, 0, 0, 0]) Human tensor([0, 1, 0, 0, 1, 0, 0, 0, 0]) Crow tensor([1, 0, 0, 0, 1, 0, 0, 0, 0]) Flying superhero tensor([1, 1, 0, 0, 1, 0, 0, 0, 0]) swap-flight tensor([0, 0, 1, 0, 0, 1, 0, 0, 0]) swap-speech tensor([0, 0, 0, 1, 0, 0, 1, 0, 0]) keep-flight tensor([0, 0, 0, 0, 0, 1, 0, 0, 0]) keep-speech tensor([0, 0, 0, 0, 0, 0, 1, 0, 0]) disobeys tensor([0, 0, 0, 0, 0, 0, 0, 0, 1]) he-is? tensor([0, 0, 0, 0, 0, 0, 0, 1, 0]) ``` $$x_p \;=\; \underbrace{c(t_p)}{\text{9 content bits}} \;\Vert\; \underbrace{e_p}{\text{6 position bits}} \;\Vert\; \underbrace{0}_{\text{7 scratch slots}}$$ ```python def embed(sentence): X = torch.zeros(len(sentence), num_bits) for current_token_position, current_token in enumerate(sentence): X[current_token_position, :9] = torch.tensor(token_to_vector[current_token], dtype=torch.float32) X[current_token_position, position_start + current_token_position] = 1 return X sentence = ["Crow", "disobeys", "keep-flight", "swap-speech", "he-is?"] X = embed(sentence) for tok, row in zip(sentence, X): print(f"{tok:14} {row[:9]} {row[position_start:position_start + L]} {row[previous_word_is_disobey:]}") ``` ```plaintext Crow tensor([1., 0., 0., 0., 1., 0., 0., 0., 0.]) tensor([1., 0., 0., 0., 0., 0.]) tensor([0., 0., 0., 0., 0., 0., 0.]) disobeys tensor([0., 0., 0., 0., 0., 0., 0., 0., 1.]) tensor([0., 1., 0., 0., 0., 0.]) tensor([0., 0., 0., 0., 0., 0., 0.]) keep-flight tensor([0., 0., 0., 0., 0., 1., 0., 0., 0.]) tensor([0., 0., 1., 0., 0., 0.]) tensor([0., 0., 0., 0., 0., 0., 0.]) swap-speech tensor([0., 0., 0., 1., 0., 0., 1., 0., 0.]) tensor([0., 0., 0., 1., 0., 0.]) tensor([0., 0., 0., 0., 0., 0., 0.]) he-is? tensor([0., 0., 0., 0., 0., 0., 0., 1., 0.]) tensor([0., 0., 0., 0., 1., 0.]) tensor([0., 0., 0., 0., 0., 0., 0.]) ``` $$\operatorname{head}(X)=\operatorname{softmax}\!\left(\frac{(XW_Q)(XW_K)^{\top}}{\sqrt{d_h}}\right)XW_V$$ ```python def softmax(z, dim=-1): z = z - z.max(dim=dim, keepdim=True).values e = torch.exp(z) return e / e.sum(dim=dim, keepdim=True) def head(X, Wq, Wk, Wv): Q, K, V = X @ Wq, X @ Wk, X @ Wv A = softmax(Q @ K.T / Wq.shape[1] ** 0.5) return A @ V, A ``` ## Layer 1 ### Head: Object Head ```python Wq_O = torch.zeros(num_bits, 2); Wq_O[idx["question"]] = torch.tensor([8.0, 0.0]) Wk_O = torch.zeros(num_bits, 2); Wk_O[idx["object"]] = torch.tensor([1.0, 0.0]) Wv_O = torch.zeros(num_bits, 2); Wv_O[idx["fly"]] = torch.tensor([1.0, 0.0]); Wv_O[idx["speak"]] = torch.tensor([0.0, 1.0]) Wo_O = torch.zeros(2, num_bits); Wo_O[0, object_attribute_fly] = 1; Wo_O[1, object_attribute_speak] = 1 print("Wq_O nonzero rows:", (Wq_O != 0).any(1).nonzero().flatten()) print("Wk_O nonzero rows:", (Wk_O != 0).any(1).nonzero().flatten()) print("Wv_O nonzero rows:", (Wv_O != 0).any(1).nonzero().flatten()) print("Wo_O nonzero cols:", (Wo_O != 0).any(0).nonzero().flatten()) ``` ```plaintext Wq_O nonzero rows: tensor([7]) Wk_O nonzero rows: tensor([4]) Wv_O nonzero rows: tensor([0, 1]) Wo_O nonzero cols: tensor([16, 17]) ``` ### Head: Disobey Position Finder $$W\_Q^{(P)}=S\\sum\_{p=1}^{L-1} e\_{,\\text{pos}*p},e*{p-1}^{\\top} \\qquad W\_K^{(P)}=\\sum\_{p=0}^{L-1} e\_{,\\text{pos}*p},e*{p}^{\\top}$$ $$M=W_Q^{(P)}W_K^{(P)\top}\quad\Longrightarrow\quad q_i\!\cdot\!k_j = S\,[\,j=i-1\,]$$ $$M\neq M^{\top}$$ ```python S = 24.0 Wq_P = torch.zeros(num_bits, L) for p in range(1, L): Wq_P[position_start + p, p - 1] = S Wk_P = torch.zeros(num_bits, L) for p in range(L): Wk_P[position_start + p, p] = 1 Wv_P = torch.zeros(num_bits, 1); Wv_P[idx["disobey"], 0] = 1 Wo_P = torch.zeros(1, num_bits); Wo_P[0, previous_word_is_disobey] = 1 M = Wq_P @ Wk_P.T print("M[9:15, 9:15] =") print(M[position_start:position_start + L, position_start:position_start + L]) print("symmetric:", torch.allclose(M, M.T)) ``` ```plaintext M[9:15, 9:15] = tensor([[ 0., 0., 0., 0., 0., 0.], [24., 0., 0., 0., 0., 0.], [ 0., 24., 0., 0., 0., 0.], [ 0., 0., 24., 0., 0., 0.], [ 0., 0., 0., 24., 0., 0.], [ 0., 0., 0., 0., 24., 0.]]) symmetric: False ``` $$\operatorname{FFN}_1(x)=0 \qquad X_1 = X + \operatorname{head}_O(X)\,W_O^{(O)} + \operatorname{head}_P(X)\,W_O^{(P)}$$ ```python def layer1(X): oO, AO = head(X, Wq_O, Wk_O, Wv_O) oP, AP = head(X, Wq_P, Wk_P, Wv_P) X1 = X + oO @ Wo_O + oP @ Wo_P return X1, AO, AP X1, AO, AP = layer1(X) print("A_P") print(AP) print() for tok, row in zip(sentence, X1): print(f"{tok:14} previous_word_is_disobey={row[previous_word_is_disobey]:.3f} " f"object_attribute_fly={row[object_attribute_fly]:.3f} " f"object_attribute_speak={row[object_attribute_speak]:.3f}") ``` ```plaintext A_P tensor([[0.20, 0.20, 0.20, 0.20, 0.20], [1.00, 0.00, 0.00, 0.00, 0.00], [0.00, 1.00, 0.00, 0.00, 0.00], [0.00, 0.00, 1.00, 0.00, 0.00], [0.00, 0.00, 0.00, 1.00, 0.00]]) Crow previous_word_is_disobey=0.200 object_attribute_fly=0.200 object_attribute_speak=0.000 disobeys previous_word_is_disobey=0.000 object_attribute_fly=0.200 object_attribute_speak=0.000 keep-flight previous_word_is_disobey=1.000 object_attribute_fly=0.200 object_attribute_speak=0.000 swap-speech previous_word_is_disobey=0.000 object_attribute_fly=0.200 object_attribute_speak=0.000 he-is? previous_word_is_disobey=0.000 object_attribute_fly=0.986 object_attribute_speak=0.000 ``` ## Layer 2 ### Two heads: Head1 - "get flight attribute" Head2 - "get speech attribute" Layer 2 has two heads: get flight attribute and get speech attribute. Usually layers have a similar topology, so two heads are used in both layers. Could this work be done with a single head, like the action head in the previous notebook? Not with this residual layout. In the previous notebook, one head attended to both action words, and that worked because each action word's own swap bit (`swap_fly` or `swap_speak`) says which attribute it swaps. Here, the disobey information sits in one shared slot, `previous_word_is_disobey`, on both action words. A single head attending to both action words would add the two disobey signals into the same number, and they could no longer be told apart. For example, `Crow disobeys keep-flight swap-speech he-is?` and `Crow keep-flight disobeys swap-speech he-is?` would give exactly the same head output, but the answers are Human and Crow. So we use one head for the flight word and one head for the speech word. ```python Wq_fly_head = torch.zeros(num_bits, 1); Wq_fly_head[idx["question"], 0] = 8 Wk_fly_head = torch.zeros(num_bits, 1); Wk_fly_head[idx["action_fly"], 0] = 1 Wv_fly_head = torch.zeros(num_bits, 2); Wv_fly_head[idx["swap_fly"]] = torch.tensor([1.0, 0.0]); Wv_fly_head[previous_word_is_disobey] = torch.tensor([0.0, 1.0]) Wq_speak_head = torch.zeros(num_bits, 1); Wq_speak_head[idx["question"], 0] = 8 Wk_speak_head = torch.zeros(num_bits, 1); Wk_speak_head[idx["action_speak"], 0] = 1 Wv_speak_head = torch.zeros(num_bits, 2); Wv_speak_head[idx["swap_speak"]] = torch.tensor([1.0, 0.0]); Wv_speak_head[previous_word_is_disobey] = torch.tensor([0.0, 1.0]) Wo_2 = torch.zeros(4, num_bits) Wo_2[0, is_swap_attr_fly] = 1 Wo_2[1, is_attr_fly_disobeyed] = 1 Wo_2[2, is_swap_attr_speak] = 1 Wo_2[3, is_attr_speak_disobeyed] = 1 print("Wv_fly_head nonzero rows: ", (Wv_fly_head != 0).any(1).nonzero().flatten()) print("Wv_speak_head nonzero rows:", (Wv_speak_head != 0).any(1).nonzero().flatten()) print("Wo_2 nonzero cols: ", (Wo_2 != 0).any(0).nonzero().flatten()) ``` ```plaintext Wv_fly_head nonzero rows: tensor([ 2, 15]) Wv_speak_head nonzero rows: tensor([ 3, 15]) Wo_2 nonzero cols: tensor([18, 19, 20, 21]) ``` $$X_2 = X_1 + \big[\operatorname{head}_F(X_1)\;\Vert\;\operatorname{head}_S(X_1)\big]\,W_O^{(2)}$$ ```python def layer2_attn(X1): oF, AF = head(X1, Wq_fly_head, Wk_fly_head, Wv_fly_head) oS, AS = head(X1, Wq_speak_head, Wk_speak_head, Wv_speak_head) X2 = X1 + torch.cat([oF, oS], dim=1) @ Wo_2 return X2, AF, AS X2, AF, AS = layer2_attn(X1) q = sentence.index("he-is?") print("A_F[q]", AF[q]) print("A_S[q]", AS[q]) print() print("is_swap_attr_fly ", X2[q, is_swap_attr_fly]) print("is_attr_fly_disobeyed ", X2[q, is_attr_fly_disobeyed]) print("is_swap_attr_speak ", X2[q, is_swap_attr_speak]) print("is_attr_speak_disobeyed", X2[q, is_attr_speak_disobeyed]) ``` ```plaintext A_F[q] tensor([0.00, 0.00, 1.00, 0.00, 0.00]) A_S[q] tensor([0.00, 0.00, 0.00, 1.00, 0.00]) is_swap_attr_fly tensor(0.) is_attr_fly_disobeyed tensor(1.00) is_swap_attr_speak tensor(1.00) is_attr_speak_disobeyed tensor(0.00) ``` ### FFN 2 The FFN is shown for completeness, also as a hand-constructed implementation. But for understanding the ideas of attention, position embedding, residuals and the need for layers, this part can be skipped. Summary: bias values are used carefully to allow distinguishing 0, 1, 2, 3. This provides different ReLU activation levels corresponding to the number of flips. This FFN is a parity finder, while the FFN used in the previous notebook without the disobeys word was an XOR gate. Repeating the note that, in the real implementation, all the weights and biases are learned using gradient descent and the backpropagation algorithm. $$s_{\text{fly}} = x_{\text{object\_attribute\fly}} + x{\text{is\_swap\_attr\fly}} + x{\text{is\_attr\fly\disobeyed}} \qquad s{\text{speak}} = x{\text{object\_attribute\speak}} + x{\text{is\_swap\_attr\speak}} + x{\text{is\_attr\_speak\_disobeyed}}$$ $$\pi(s)=\operatorname{ReLU}(s)-2\operatorname{ReLU}(s-1)+2\operatorname{ReLU}(s-2)-2\operatorname{ReLU}(s-3)$$ $$\pi(0)=0,\quad \pi(1)=1,\quad \pi(2)=0,\quad \pi(3)=1$$ ```python W1 = torch.zeros(num_bits, 8) b1 = torch.zeros(8) for r in (object_attribute_fly, is_swap_attr_fly, is_attr_fly_disobeyed): W1[r, 0:4] = 1 for r in (object_attribute_speak, is_swap_attr_speak, is_attr_speak_disobeyed): W1[r, 4:8] = 1 b1[0:4] = torch.tensor([0.0, -1.0, -2.0, -3.0]) b1[4:8] = torch.tensor([0.0, -1.0, -2.0, -3.0]) W2 = torch.zeros(8, 2) W2[0:4, 0] = torch.tensor([1.0, -2.0, 2.0, -2.0]) W2[4:8, 1] = torch.tensor([1.0, -2.0, 2.0, -2.0]) print("W1.T\n", W1.T) print("\nb1", b1) print("\nW2.T\n", W2.T) print("\npi:", [float(torch.relu(torch.tensor([s, s - 1.0, s - 2.0, s - 3.0])) @ W2[0:4, 0]) for s in range(4)]) ``` ```plaintext W1.T tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 1., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 1., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 1., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 1., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 1.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 1.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 1.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 1.]]) b1 tensor([ 0., -1., -2., -3., 0., -1., -2., -3.]) W2.T tensor([[ 1., -2., 2., -2., 0., 0., 0., 0.], [ 0., 0., 0., 0., 1., -2., 2., -2.]]) pi: [0.0, 1.0, 0.0, 1.0] ``` ## Forward ```python def forward(sentence): X = embed(sentence) X1, AO, AP = layer1(X) X2, AF, AS = layer2_attn(X1) H = torch.relu(X2 @ W1 + b1) Y = H @ W2 return dict(X=X, X1=X1, X2=X2, Y=Y, AO=AO, AP=AP, AF=AF, AS=AS) word_to_attributes = { "Rock": (0, 0, 0), "Human": (0, 0, 1), "Car": (0, 1, 0), "Talking Tow Truck": (0, 1, 1), "Crow": (1, 0, 0), "Flying Superhero": (1, 0, 1), "Plane": (1, 1, 0), "Talking Planes": (1, 1, 1), } attributes_to_word = {b: w for w, b in word_to_attributes.items()} def readout(sentence): r = forward(sentence) q = sentence.index("he-is?") fly, speak = (int(v.round()) for v in r["Y"][q]) return attributes_to_word[(fly, 0, speak)], r["Y"][q] ``` ```python sentences = [ ["Human", "disobeys", "keep-flight", "disobeys", "swap-speech", "he-is?"], ["Crow", "disobeys", "keep-flight", "swap-speech", "he-is?"], ["Crow", "keep-flight", "disobeys", "swap-speech", "he-is?"], ["Crow", "disobeys", "keep-flight", "disobeys", "swap-speech", "he-is?"], ] for s in sentences: word, y = readout(s) print(f"{' '.join(s):58} {y} -> {word}") ``` ```plaintext Human disobeys keep-flight disobeys swap-speech he-is? tensor([1.00, 0.98]) -> Flying Superhero Crow disobeys keep-flight swap-speech he-is? tensor([0.02, 1.00]) -> Human Crow keep-flight disobeys swap-speech he-is? tensor([0.99, 0.00]) -> Crow Crow disobeys keep-flight disobeys swap-speech he-is? tensor([0.02, 0.00]) -> Rock ``` $$\{\text{Crow},\,\text{disobeys},\,\text{keep-flight},\,\text{swap-speech},\,\text{he-is?}\}$$ ```python a = ["Crow", "disobeys", "keep-flight", "swap-speech", "he-is?"] b = ["Crow", "keep-flight", "disobeys", "swap-speech", "he-is?"] print(sorted(a) == sorted(b)) print(readout(a)[0]) print(readout(b)[0]) ``` ```plaintext True Human Crow ``` ```python for s in sentences: r = forward(s) print(" ".join(s)) print(" A_P") for tok, row in zip(s, r["AP"]): print(f" {tok:14} {row}") print(" previous_word_is_disobey", r["X1"][:, previous_word_is_disobey]) q = s.index("he-is?") print(" gathered ", r["X2"][q, [is_swap_attr_fly, is_attr_fly_disobeyed, is_swap_attr_speak, is_attr_speak_disobeyed]]) print(" Y ", r["Y"][q]) print() ``` ```plaintext Human disobeys keep-flight disobeys swap-speech he-is? A_P Human tensor([0.17, 0.17, 0.17, 0.17, 0.17, 0.17]) disobeys tensor([1.00, 0.00, 0.00, 0.00, 0.00, 0.00]) keep-flight tensor([0.00, 1.00, 0.00, 0.00, 0.00, 0.00]) disobeys tensor([0.00, 0.00, 1.00, 0.00, 0.00, 0.00]) swap-speech tensor([0.00, 0.00, 0.00, 1.00, 0.00, 0.00]) he-is? tensor([0.00, 0.00, 0.00, 0.00, 1.00, 0.00]) previous_word_is_disobey tensor([0.33, 0.00, 1.00, 0.00, 1.00, 0.00]) gathered tensor([0.00, 1.00, 1.00, 1.00]) Y tensor([1.00, 0.98]) Crow disobeys keep-flight swap-speech he-is? A_P Crow tensor([0.20, 0.20, 0.20, 0.20, 0.20]) disobeys tensor([1.00, 0.00, 0.00, 0.00, 0.00]) keep-flight tensor([0.00, 1.00, 0.00, 0.00, 0.00]) swap-speech tensor([0.00, 0.00, 1.00, 0.00, 0.00]) he-is? tensor([0.00, 0.00, 0.00, 1.00, 0.00]) previous_word_is_disobey tensor([0.20, 0.00, 1.00, 0.00, 0.00]) gathered tensor([0.00, 1.00, 1.00, 0.00]) Y tensor([0.02, 1.00]) Crow keep-flight disobeys swap-speech he-is? A_P Crow tensor([0.20, 0.20, 0.20, 0.20, 0.20]) keep-flight tensor([1.00, 0.00, 0.00, 0.00, 0.00]) disobeys tensor([0.00, 1.00, 0.00, 0.00, 0.00]) swap-speech tensor([0.00, 0.00, 1.00, 0.00, 0.00]) he-is? tensor([0.00, 0.00, 0.00, 1.00, 0.00]) previous_word_is_disobey tensor([0.20, 0.00, 0.00, 1.00, 0.00]) gathered tensor([0.00, 0.00, 1.00, 1.00]) Y tensor([0.99, 0.00]) Crow disobeys keep-flight disobeys swap-speech he-is? A_P Crow tensor([0.17, 0.17, 0.17, 0.17, 0.17, 0.17]) disobeys tensor([1.00, 0.00, 0.00, 0.00, 0.00, 0.00]) keep-flight tensor([0.00, 1.00, 0.00, 0.00, 0.00, 0.00]) disobeys tensor([0.00, 0.00, 1.00, 0.00, 0.00, 0.00]) swap-speech tensor([0.00, 0.00, 0.00, 1.00, 0.00, 0.00]) he-is? tensor([0.00, 0.00, 0.00, 0.00, 1.00, 0.00]) previous_word_is_disobey tensor([0.33, 0.00, 1.00, 0.00, 1.00, 0.00]) gathered tensor([0.00, 1.00, 1.00, 1.00]) Y tensor([0.02, 0.00]) ``` * * * ### About this file This notebook is part of the support files for the TechAarvam workshop [**Build Your Own Model**](https://www.techaarvam.com/workshops/build-your-own-model). * Website: [https://www.techaarvam.com](https://www.techaarvam.com) * Workshop files repository: [https://github.com/techaarvam/byom\_workshop](https://github.com/techaarvam/byom_workshop) * YouTube: [https://www.youtube.com/@TechAarvam](https://www.youtube.com/@TechAarvam) © TechAarvam. You are free to use, copy, modify, share and build on this material, including for commercial purposes, **provided you credit TechAarvam** and link back to [https://www.techaarvam.com](https://www.techaarvam.com). Please keep this notice with any copy or derivative. Provided as-is, without warranty.