重新整理文件夹结构，并添加CNN训练代码。

tools/TrainCNN/backward.py

@@ -0,0 +1,123 @@
+import tensorflow as tf
+from progressive.bar import Bar
+import generate
+import forward
+
+
+def save_kernal(fp, val):
+    print(val.shape[2], file=fp)
+    print(val.shape[3], file=fp)
+    print(val.shape[1], file=fp)
+    print(val.shape[0], file=fp)
+    for in_channel in range(val.shape[2]):
+        for out_channel in range(val.shape[3]):
+            for row in range(val.shape[0]):
+                for col in range(val.shape[1]):
+                    print(val[row][col][in_channel][out_channel], file=fp)
+
+
+def save_weight_mat(fp, val):
+    print(val.shape[0], file=fp)
+    print(val.shape[1], file=fp)
+    for row in range(val.shape[0]):
+        for col in range(val.shape[1]):
+            print(val[row][col], file=fp)
+
+
+def save_bias(fp, val):
+    print(val.shape[0], file=fp)
+    for i in range(val.shape[0]):
+        print(val[i], file=fp)
+
+
+def save_para(folder, paras):
+    with open(folder + "/conv1_w", "w") as fp:
+        save_kernal(fp, paras[0])
+    with open(folder + "/conv1_b", "w") as fp:
+        save_bias(fp, paras[1])
+    with open(folder + "/conv2_w", "w") as fp:
+        save_kernal(fp, paras[2])
+    with open(folder + "/conv2_b", "w") as fp:
+        save_bias(fp, paras[3])
+    with open(folder + "/fc1_w", "w") as fp:
+        save_weight_mat(fp, paras[4])
+    with open(folder + "/fc1_b", "w") as fp:
+        save_bias(fp, paras[5])
+    with open(folder + "/fc2_w", "w") as fp:
+        save_weight_mat(fp, paras[6])
+    with open(folder + "/fc2_b", "w") as fp:
+        save_bias(fp, paras[7])
+
+
+STEPS = 30000
+BATCH = 10
+LEARNING_RATE_BASE  = 0.01
+LEARNING_RATE_DECAY = 0.99
+MOVING_AVERAGE_DECAY = 0.99
+
+
+def train(dataset, show_bar=False):
+    test_images, test_labels = dataset.all_test_sets()
+
+    x = tf.placeholder(tf.float32, [None, forward.SRC_ROWS, forward.SRC_COLS, forward.SRC_CHANNELS])
+    y_= tf.placeholder(tf.float32, [None, forward.OUTPUT_NODES])
+    nodes, vars = forward.forward(0.001)
+    y = nodes[-1]
+
+    ce  = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
+    cem = tf.reduce_mean(ce)
+    loss= cem + tf.add_n(tf.get_collection("losses"))
+
+    global_step = tf.Variable(0, trainable=False)
+    learning_rate = tf.train.exponential_decay(
+        LEARNING_RATE_BASE,
+        global_step,
+        len(dataset.train_sets) / BATCH,
+        LEARNING_RATE_DECAY,
+        staircase=False)
+    train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss, global_step=global_step)
+
+    ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
+    ema_op = ema.apply(tf.trainable_variables())
+    with tf.control_dependencies([train_step, ema_op]):
+        train_op = tf.no_op(name='train')
+
+    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
+    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+    acc = 0
+
+    with tf.Session() as sess:
+        init_op = tf.global_variables_initializer()
+        sess.run(init_op)
+
+        if show_bar:
+            bar = Bar(max_value=STEPS, width=u'50%')
+            bar.cursor.clear_lines(1)
+            bar.cursor.save()
+
+        for i in range(STEPS):
+            images_samples, labels_samples = dataset.sample_train_sets(BATCH)
+
+            _, loss_value, step = sess.run(
+                [train_op, loss, global_step],
+                feed_dict={x: images_samples, y_: labels_samples}
+            )
+
+            if i % 100 == 0:
+                if i % 1000 == 0:
+                    acc = sess.run(accuracy, feed_dict={x: test_images, y_: test_labels})
+
+            if show_bar:
+                bar.title = "step: %d, loss: %f, acc: %f" % (step, loss_value, acc)
+                bar.cursor.restore()
+                bar.draw(value=i+1)
+
+        vars_val = sess.run(vars)
+        save_para("paras", vars_val)
+        # nodes_val = sess.run(nodes, feed_dict={x:test})
+        # return vars_val, nodes_val
+
+
+if __name__ == "__main__":
+    dataset = generate.DataSet("images")
+    train(dataset, show_bar=True)