Working version of this script

1 files changed, 49 insertions, 27 deletions

diff --git a/src/train5.py b/src/train5.py
index 628b0b6..03e9669 100644
--- a/src/train5.py
+++ b/src/train5.py
@@ -2,6 +2,8 @@
 import pandas as pd
 import numpy as np
 
+from tensorflow import transpose
+
 from tensorflow.keras.backend import get_value
 from tensorflow.keras.models import Model
 from tensorflow.keras.layers import Concatenate
@@ -11,6 +13,27 @@ from tensorflow.keras.layers import Convolution2D
 from tensorflow.keras.layers import Flatten
 
 
+def load_data(size):
+    shape = (-1, size, size, 8)
+
+    tr_fn = "training-"+str(size)+".csv"
+    training_csv = pd.read_csv(tr_fn) # .head(80000)
+    training_data = training_csv
+    training_stack_input = np.array(training_data.iloc[:, 2:-2]).reshape(shape, order='F')
+    training_flats_input = np.array(training_data.iloc[:, 0:2])
+    training_input = [training_stack_input, training_flats_input]
+    training_outcome = training_data.iloc[:, -2:]
+
+    val_fn = "validation-"+str(size)+".csv"
+    val_data = pd.read_csv(val_fn).tail(20000)
+    val_stack_input = np.array(val_data.iloc[:, 2:-2]).reshape(shape, order='F')
+    val_flats_input = np.array(val_data.iloc[:, 0:2])
+    val_input = [val_stack_input, val_flats_input]
+    val_outcome = val_data.iloc[:, -2:]
+
+    return [(training_input, training_outcome), (val_input, val_outcome)]
+
+
 def make_model(size, magic=[9, 18, 9]):
     # Our model for the stacks, a small CNN
     stack_shape = (size, size, 8)
@@ -38,6 +61,7 @@ def make_model(size, magic=[9, 18, 9]):
     model.summary()
     return model
 
+
 def train(size, model, data, iterations=1, epochs=10):
     (training_input, training_outcome), (val_input, val_outcome) = data
     results = []
@@ -54,33 +78,13 @@ def train(size, model, data, iterations=1, epochs=10):
     return (results, model)
 
 
-def load_data(size):
-    size = 5
-    shape = (-1, size, size, 8)
-
-    tr_fn = "training-"+str(size)+".csv"
-    training_csv = pd.read_csv(tr_fn) # .head(80000)
-    training_data = training_csv
-    training_stack_input = np.array(training_data.iloc[:, 2:-2]).reshape(shape, order='F')
-    training_flats_input = np.array(training_data.iloc[:, 0:2])
-    training_input = [training_stack_input, training_flats_input]
-    training_outcome = training_data.iloc[:, -2:]
-
-    val_fn = "validation-"+str(size)+".csv"
-    val_data = pd.read_csv(val_fn).tail(20000)
-    val_stack_input = np.array(val_data.iloc[:, 2:-2]).reshape(shape, order='F')
-    val_flats_input = np.array(val_data.iloc[:, 0:2])
-    val_input = [val_stack_input, val_flats_input]
-    val_outcome = val_data.iloc[:, -2:]
-
-    return [(training_input, training_outcome), (val_input, val_outcome)]
-
 def model_size(model):
     return sum([np.prod(get_value(w).shape) for w in model.trainable_weights])
 
+
 def magic_search(data):
     results = []
-    for width in range(8, 16):
+    for width in range(10, 16):
         for dense1 in range(8, 24):
             for dense2 in range(6, dense1+1):
                 m = make_model(5, [width, dense1, dense2])
@@ -92,14 +96,32 @@ def magic_search(data):
         print("Parameters {0}: {1}".format(r[0], r[1][-1:][0]))
 
 
+def write_weights(model):
+    def fix(string):
+        return string.replace("[", "{").replace("]", "}")
+    f = open("weights.txt", "w")
+    conv2d_weights = np.array(transpose(model.trainable_variables[0], perm=[3, 1, 0, 2]))
+    conv2d_biases = np.array(model.trainable_variables[1])
+    dense1_weights = np.array(transpose(model.trainable_variables[2], perm=[1, 0]))
+    dense1_biases = np.array(model.trainable_variables[3])
+    dense2_weights = np.array(transpose(model.trainable_variables[4], perm=[1, 0]))
+    dense2_biases = np.array(model.trainable_variables[5])
+    output_weights = np.array(transpose(model.trainable_variables[6], perm=[1, 0]))
+    output_biases = np.array(model.trainable_variables[7])
+    for v in [conv2d_weights, conv2d_biases,
+              dense1_weights, dense1_biases,
+              dense2_weights, dense2_biases,
+              output_weights, output_biases]:
+        f.write(fix(str(v))+"\n")
+    f.close()
+
+
 data = load_data(5)
 model = make_model(5, [12, 9, 8])
-results, model = train(5, model, data, iterations=1, epochs=20)
+results, model = train(5, model, data, iterations=10, epochs=20)
 print("\nScores")
 for i in range(len(results)):
     print("Iteration {0}: {1}".format(i+1, results[i]))
+write_weights(model)
 
-f=open("softmax_weights.txt","w")
-f.write(str(model.trainable_variables))
-f.close()
-# print(model.trainable_variables)
+# magic_search(data)