tensorflow/transformer-attention1.pyHEADmaster

1 files changed, 54 insertions, 0 deletions

diff --git a/tensorflow/transformer-attention1.py b/tensorflow/transformer-attention1.py
new file mode 100644
index 0000000..32fe739
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+++ b/tensorflow/transformer-attention1.py
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+import numpy as np
+import math
+
+dmodel = 32
+num_heads = 2
+embedding_dim = dmodel #dmodel // num_heads
+nwords = 4
+
+#assert(dmodel/num_heads == embedding_dim)
+
+states = np.array([np.ones(shape=[embedding_dim])*(i*0.1) for i in range(nwords)]) # num. words x embedding dim
+
+Wqs = []
+Wks = []
+Wvs = []
+scores = []
+
+
+def softmax(m):
+    return np.exp(m) / np.sum(np.exp(m), axis=1)
+
+for h in range(num_heads):
+    Wq = np.random.rand(embedding_dim, int(dmodel/num_heads))
+    Wk = np.random.rand(embedding_dim, int(dmodel/num_heads))
+    Wv = np.random.rand(embedding_dim, int(dmodel/num_heads))
+
+    queries = np.matmul(states, Wq)
+    keys    = np.matmul(states, Wk)
+    print(states.shape)
+    values  = np.matmul(states, Wv)
+    print(values.shape)
+    exit()
+
+    out = np.matmul(queries, np.transpose(keys))
+    out = out/math.sqrt(dmodel)
+
+    # manual
+    #out_max = []
+    #for i in range(out.shape[0]):
+    #    out_max.append(softmax(out[i]))
+    #out = np.array(out_max)
+
+    out = softmax(out)
+    out = np.matmul(out, values)
+
+    Wqs.append(Wq)
+    Wks.append(Wk)
+    Wvs.append(Wv)
+    scores.append(out)
+
+out = np.concatenate(scores, axis=0)
+out = np.matmul(np.random.rand(nwords, out.shape[0]), out)
+
+