resolved conflict

scripts/train_test_model_multi.py

@@ -0,0 +1,72 @@
+#!/usr/bin/env python
+
+import sys
+import os
+import numpy as np
+from keras.datasets import mnist
+from keras.utils import np_utils
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.layers import Dropout
+
+def softmax(x):
+    return np.exp(x) / np.sum(np.exp(x), axis=1)[:, None]
+
+
+def baseline_model(pixels_count, classes_count):
+    model = Sequential()
+    model.add(Dense(100, input_dim=pixels_count, init='normal', activation='tanh'))
+    model.add(Dense(classes_count, input_dim=100, init='normal', activation='softmax'))
+    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+    return model
+
+
+if __name__ == "__main__":
+    ### Load trainset from mnist
+
+    (X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+    ### Flatten pictures into vectors
+   
+    pixels_count = X_train.shape[1] * X_train.shape[2]
+    X_train = X_train.reshape(X_train.shape[0], pixels_count).astype('float32')
+    print "X shape: ", X_train.shape
+
+    X_test = X_test.reshape(X_test.shape[0], pixels_count).astype('float32')
+
+    ### Normalize data to (0, 1)
+
+    X_train = X_train / 255
+    X_test = X_test / 255
+
+    ### Change classes to one hot encoding matrixes
+
+    y_train = np_utils.to_categorical(y_train)
+    classes_count = y_train.shape[1]
+    print "Y shape: ", y_train.shape
+    
+    y_test = np_utils.to_categorical(y_test)
+
+    # Train weight matrix
+
+    # Build the model
+    model = baseline_model(pixels_count, classes_count)
+    # Fit the model
+    model.fit(X_train, y_train, validation_data=(X_test, y_test), nb_epoch=10, batch_size=200, verbose=2)
+    # Final evaluation of the model
+    scores = model.evaluate(X_test, y_test, verbose=0)
+    print("Baseline Error: %.2f%%" % (100-scores[1]*100))
+
+    ### Weight and bias matrixes - we extract them from the model
+    
+    # weights_ones = np.ones((pixels_count, classes_count))
+    # print weights_ones.shape
+
+    weights1, bias1, weights2, bias2 = model.get_weights()
+    ### Save model to npz files
+    if not os.path.exists("test_model_multi"):
+        os.makedirs("test_model_multi")
+    # np.savez("test_model_multi/model", *model)
+    np.savez("test_model_multi/model", weights1 = weights1, bias1 = bias1, weights2 = weights2, bias2 = bias2)
+
+    print "Model saved! Check test_model_multi directory"

scripts/train_test_model_single.py

@@ -84,8 +84,8 @@ if __name__ == "__main__":
     # print np.count_nonzero(lr)i
 
     ### Save model to npz files
-    if not os.path.exists("test_model"):
-        os.makedirs("test_model")
-    np.savez("test_model/model", weights = weights, bias = bias)
+    if not os.path.exists("test_model_single"):
+        os.makedirs("test_model_single")
+    np.savez("test_model_single/model", weights = weights, bias = bias)
 
-    print "Model saved! Check test_model directory"
+    print "Model saved! Check test_model_single directory"

src/marian.h

@@ -5,4 +5,5 @@
 #include "graph_operators.h"
 #include "expressions.h"
 #include "expression_operators.h"
+#include "param_initializers.h"
 

src/param_initializers.h

@@ -0,0 +1,34 @@
+#pragma once
+
+#include <random>
+#include <algorithm>
+#include <iterator>
+#include <functional>
+
+#include "tensor.h"
+
+namespace marian {
+
+void zeros(Tensor t) {
+  std::vector<float> vals(t.size(), 0.0f);
+  thrust::copy(vals.begin(), vals.end(), t.begin());
+}
+
+void ones(Tensor t) {
+  std::vector<float> vals(t.size(), 1.0f);
+  thrust::copy(vals.begin(), vals.end(), t.begin());
+}
+
+void randreal(Tensor t) {
+  std::random_device device;
+  std::default_random_engine engine(device());
+  std::uniform_real_distribution<> dist(0, 1);
+  auto gen = std::bind(dist, engine);
+
+  std::vector<float> vals(t.size());
+  std::generate(begin(vals), end(vals), gen);
+
+  thrust::copy(vals.begin(), vals.end(), t.begin());
+}
+
+} // namespace marian