still very bad

.gitignore

@@ -1,3 +1,6 @@
+*.csv
+*.png
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

fromNotebook.py

@@ -6,7 +6,7 @@
 
 import matplotlib.pyplot as plt
 
-print("-------------start-------------")
+print("\n           -------------start-------------")
 # For reproducibility
 np.random.seed(1237)
 
@@ -24,9 +24,9 @@
 #merge the input-columns into one big string column 'merged'
 df["merged"] = df["CITY"].map(str) + ' ' + df["INSTITUTION_FULL"] + ' ' + df["ADDRESS_FULL"]
 
-df.head(9)
-
-
+print(df.head(9))
+df.to_csv('merged.csv')
+df.reindex(np.random.permutation(df.index)) # shuffle the rows, because I suspect some row-dependence...
 
 # prepare data for keras:
 
@@ -43,7 +43,7 @@
 
 # 32 states and NA
 num_labels = 23   # there are only 25 states with institiutions in the dataset
-vocab_size = 10000
+vocab_size = 3000  # how many different words to keep?
 batch_size = 150
 
 from keras.preprocessing.text import Tokenizer
@@ -81,12 +81,12 @@
 print("\n\n ---------- and now the actual keras training: ----------- \n\n")
 
 model = Sequential()
-model.add(Dense(int(512/3), input_shape=(vocab_size,)))
+model.add(Dense(int(512/2), input_shape=(vocab_size,)))
+model.add(Activation('relu'))
+model.add(Dropout(0.4))
+model.add(Dense(int(256/2)))
 model.add(Activation('relu'))
-model.add(Dropout(0.3))
-#model.add(Dense(int(256/2)))
-#model.add(Activation('relu'))
-#model.add(Dropout(0.3))
+model.add(Dropout(0.2))
 model.add(Dense(num_labels))
 model.add(Activation('softmax'))
 model.summary()
@@ -135,7 +135,8 @@
     #print(test_files_names.iloc[i])
     #print(f"\n {i} input-text: {train_input[i]} " )#+ train_input[i])
     #print(f"x_train {i}: {x_train[i]},\n y_train i: {y_train[i]}")
-    print('Label:' + test_tags.iloc[i].ljust(19) + " -Predicted label: " + predicted_label.ljust(19) + f" {np.max(prediction[0])}")
+    print('Label:' + test_tags.iloc[i].ljust(19) + " -Predicted label: "
+     + predicted_label.ljust(19) + f" {np.max(prediction[0]):4.2f}")
     #print(prediction)
 
 print(text_labels)