added some simple toy program for testing purpose

test_dropout.py

@@ -18,7 +18,8 @@
                 outputs = l.output)
 
 print x_val
-print f(x_val)
+print f(x_val) 
+print f(x_val) # second time should be different
 
 
 

test_embedding.py

@@ -0,0 +1,120 @@
+import sys
+import math
+import numpy as np
+import theano
+import theano.tensor as T
+
+from cnn4nlp import (WordEmbeddingLayer,
+                     ConvFoldingPoolLayer)
+
+from logreg import LogisticRegression
+
+rng = np.random.RandomState(1234)
+
+##### Test Part One ###############
+# WordEmbeddingLayer
+#################################
+
+EMB_DIM = 6
+x = T.imatrix('x') # the vector of word indices
+
+l1 = WordEmbeddingLayer(rng, 
+                        x,
+                        10, EMB_DIM)
+
+print "######## All Embeddings ########"
+print l1.embeddings.get_value()
+print l1.embeddings.get_value().shape
+
+
+
+get_embedding = theano.function(
+    inputs = [x], 
+    outputs = l1.output,
+    # mode = "DebugMode"
+)
+
+print "######## Selected Embeddings ########"
+selected_embedding =  get_embedding(
+    np.array([
+        [1,3,5], 
+        [2,0,7]
+    ], 
+             dtype = np.int32)
+)
+
+print selected_embedding
+print selected_embedding.shape
+assert selected_embedding.shape == (2, 1, EMB_DIM, 3)
+
+
+##### Test Part Two ###############
+# ConvFoldingPoolLayer
+#################################
+
+print "############# ConvFoldingPoolLayer ##############"
+k = 2
+feat_map_n = 2
+l2 = ConvFoldingPoolLayer(rng, 
+                          input = l1.output, 
+                          filter_shape = (feat_map_n, 1, 1, 2),  # two feature map, height: 1, width: 2, 
+                          k = k
+)
+
+l2_output = theano.function(
+    inputs = [x],
+    outputs = l2.output,
+)
+
+# TODO:
+# check the dimension
+# input: 1 x 1 x 6 x 4
+out = l2_output(
+    np.array([[1, 3, 4, 5]], dtype = np.int32)
+)
+
+print out
+print out.shape
+
+expected = (1, feat_map_n, EMB_DIM / 2, k)
+assert out.shape == expected, "%r != %r" %(out.shape, expected)
+
+##### Test Part Three ###############
+# LogisticRegressionLayer
+#################################
+
+print "############# LogisticRegressionLayer ##############"
+
+l3 = LogisticRegression(
+    rng, 
+    input = l2.output.flatten(2), 
+    n_in = feat_map_n * k * EMB_DIM / 2, # we fold once, so divide by 2
+    n_out = 5 # five sentiment level
+)
+
+print "n_in = %d" %(2 * 2 * math.ceil(EMB_DIM / 2.))
+
+y = T.ivector('y') # the sentence sentiment label
+
+p_y_given_x = theano.function(
+    inputs = [x],
+    outputs = l3.p_y_given_x,
+    mode = "DebugMode"
+)
+
+print "p_y_given_x = "
+print p_y_given_x(
+    np.array([[1, 3, 4, 5], [0, 1, 4 ,7]], dtype = np.int32)
+)
+
+cost = theano.function(
+    inputs = [x, y],
+    outputs = l3.nnl(y),
+    mode = "DebugMode"
+)
+
+print "cost:\n", cost(
+    np.array([[1, 3, 4, 5], [0, 1, 4 ,7]], dtype = np.int32), 
+    np.array([1, 2], dtype = np.int32)
+)
+