add visualization script

visualize.py

@@ -0,0 +1,74 @@
+from keras.models import Model
+import numpy as np; np.random.seed(0)
+import seaborn as sns; sns.set()
+import pandas as pd
+import math as math
+
+intermediate_layer_model2 = Model(inputs=model.input,
+                                 outputs=model.layers[2].output)
+
+intermediate_layer_model1 = Model(inputs=model.input,
+                                 outputs=model.layers[1].output)
+
+def show(sentences): 
+    fig, axn = plt.subplots(len(sentences), 1)
+    fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=5, hspace=10)
+    fig.tight_layout()
+
+    for i, ax in enumerate(axn.flat):
+        seq = sentences[i]
+        words = seq.split(" ")
+        arr = numpy.zeros(35)
+        for j in range(len(words)):
+            if words[j] in word_to_idx:
+                arr[j] = word_to_idx[words[j].lower()]
+            else:
+                arr[j] = word_to_idx[""]
+
+        arr = numpy.reshape(arr, (1, arr.shape[0]))
+        intermediate_output2 = intermediate_layer_model2.predict(arr, verbose=0)
+        intermediate_output1 = intermediate_layer_model1.predict(arr, verbose=0)
+        print(seq, model.predict(arr))
+
+        weights = intermediate_output2 / intermediate_output1
+        val = []
+        total = 0
+        for j in range(len(words)):
+            val.append(weights[0][j][0])
+            total += weights[0][j][0]
+
+        d = {}
+        print(val)
+        d[""] = pd.Series(val, index=words)
+
+        df = pd.DataFrame(d)
+        df.reindex(sentences[i].split(" "))
+        df = df.transpose()
+
+    #     sns.heatmap(df, ax=ax, annot=False, linewidths=.0, cbar_ax=cbar_ax if i else cbar_ax, cmap="RdBu_r",#"YlGnBu",
+    #                   cbar_kws={"orientation": "vertical"})
+        sns.heatmap(df, ax=ax, annot=False, cbar_ax=None, cbar=False, linewidths=.0, cmap="YlGnBu")#"Oranges")#"RdBu_r")
+
+    return fig
+
+stressed = ["Number one being employment after graduation",
+        "My parents give me a lot of pressure",
+        "So yeah this course is actually very difficult",
+        "And I will be very stressed out",
+        "uh Currently I'm very anxious about several things",
+        "i am not stressed"]
+
+unstressed = ["It is got a rate of amazing food the culture is very good",
+        "Eh it makes me feel relaxed and uh I enjoy those things",
+        "And it has a really calming effect",
+        "It's really fun yeah",
+        "I just played around a lot"]
+
+figure = None
+figure = show(stressed)
+figure.savefig('graphs/stressed.pdf', format='pdf', dpi=300)
+figure = show(unstressed)
+figure.savefig('graphs/unstressed.pdf', format='pdf', dpi=300)
+
+# figure = show(x)
+# figure.savefig('graphs/x.pdf', format='pdf', dpi=300)