diff --git a/OpenCVTensorflowDeeplearning/Chapter11/model/checkpoint b/OpenCVTensorflowDeeplearning/Chapter11/model/checkpoint new file mode 100644 index 0000000..4a26c3c --- /dev/null +++ b/OpenCVTensorflowDeeplearning/Chapter11/model/checkpoint @@ -0,0 +1,2 @@ +model_checkpoint_path: "p11-10-LeNet.ckpt" +all_model_checkpoint_paths: "p11-10-LeNet.ckpt" diff --git a/OpenCVTensorflowDeeplearning/Chapter11/model/p11-10-LeNet.ckpt.data-00000-of-00001 b/OpenCVTensorflowDeeplearning/Chapter11/model/p11-10-LeNet.ckpt.data-00000-of-00001 new file mode 100644 index 0000000..022be70 Binary files /dev/null and b/OpenCVTensorflowDeeplearning/Chapter11/model/p11-10-LeNet.ckpt.data-00000-of-00001 differ diff --git a/OpenCVTensorflowDeeplearning/Chapter11/model/p11-10-LeNet.ckpt.index b/OpenCVTensorflowDeeplearning/Chapter11/model/p11-10-LeNet.ckpt.index new file mode 100644 index 0000000..7676cb3 Binary files /dev/null and b/OpenCVTensorflowDeeplearning/Chapter11/model/p11-10-LeNet.ckpt.index differ diff --git a/OpenCVTensorflowDeeplearning/Chapter11/model/p11-10-LeNet.ckpt.meta b/OpenCVTensorflowDeeplearning/Chapter11/model/p11-10-LeNet.ckpt.meta new file mode 100644 index 0000000..2cb4b22 Binary files /dev/null and b/OpenCVTensorflowDeeplearning/Chapter11/model/p11-10-LeNet.ckpt.meta differ diff --git a/OpenCVTensorflowDeeplearning/Chapter11/p11-10.ipynb b/OpenCVTensorflowDeeplearning/Chapter11/p11-10.ipynb index 41daad4..4b846de 100644 --- a/OpenCVTensorflowDeeplearning/Chapter11/p11-10.ipynb +++ b/OpenCVTensorflowDeeplearning/Chapter11/p11-10.ipynb @@ -57,13 +57,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Tensor(\"MaxPool:0\", shape=(?, 14, 14, 6), dtype=float32)\n", + "Tensor(\"AvgPool:0\", shape=(?, 14, 14, 6), dtype=float32)\n", "Tensor(\"Sigmoid_1:0\", shape=(?, 14, 14, 16), dtype=float32)\n" ] } ], "source": [ - "maxPool2 = tf.nn.max_pool(h_conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding=\"SAME\")\n", + "maxPool2 = tf.nn.avg_pool(h_conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding=\"SAME\")\n", "print(maxPool2)\n", "filter2 = tf.Variable(tf.truncated_normal([ 5, 5, 6, 16]))\n", "bias2 = tf.Variable(tf.truncated_normal([16]))\n", @@ -196,39 +196,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "time: 28.106985807418823\n" + "Train Accuracy( 0.98 )>0.98 breaked.\n", + "Time: 114.19587898254395\n", + "Time: 114.19613695144653\n" ] - }, - { - "data": { - "text/plain": [ - ">" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ "train_accuracy_list = []\n", "train_accuracy_x = []\n", "start_time = time.time()\n", - "for i in range(500):\n", + "for i in range(5000):\n", " batch_xs, batch_ys = mnist_data_set.train.next_batch(200)\n", " if i%2 == 0:\n", " train_accuracy = accuracy.eval(feed_dict={x:batch_xs, y_:batch_ys})\n", " # end_time = time.time()\n", " train_accuracy_list.append(train_accuracy)\n", " train_accuracy_x.append(i)\n", + " if 0.98500:\n", + " print(\"Train Accuracy(\", train_accuracy, \")>0.98 breaked.\\nTime:\", (time.time()-start_time))\n", + " break\n", " # print(\"step %d, training accuracy %g\"%(i, train_accuracy))\n", " # print(\"time:\", (end_time-start_time))\n", " # start_time = end_time\n", " # train_step.run(feed_dict={x:batch_xs, y_:batch_ys})\n", " sess.run(train_step, feed_dict={x:batch_xs, y_:batch_ys})\n", "\n", - "print(\"time:\", (time.time()-start_time))\n", - "sess.close" + "print(\"Time:\", (time.time()-start_time))" ] }, { @@ -238,7 +232,27 @@ "outputs": [ { "data": { - "image/png": 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\n", + "text/plain": [ + "'./model/p11-10-LeNet.ckpt'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.train.Saver().save(sess, \"./model/p11-10-LeNet.ckpt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" ] @@ -256,15 +270,16 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "time: 0.08179402351379395\n", - "[6 0 4 3 7 7 3 2 8 3] [6 0 9 3 7 7 2 2 7 8]\n" + "Time: 0.04697465896606445\n", + "Test: [0 5 4 8 3 1 8 1 5 2 3 5 8 9 5 5 8 9 8 5 0 5 8 4 1 2 9 3 1 7 0 5 5 3 1]\n", + "Real: [0 5 4 8 3 1 8 1 5 2 3 5 8 9 8 5 8 9 8 5 0 5 8 4 6 3 9 3 1 7 0 5 5 3 1]\n" ] } ], @@ -272,10 +287,11 @@ "start_time = time.time()\n", "test = tf.argmax(y_conv,1)\n", "tcorrect = tf.argmax(y_,1)\n", - "tbatch_xs, tbatch_ys = mnist_data_set.test.next_batch(10)\n", + "tbatch_xs, tbatch_ys = mnist_data_set.test.next_batch(35)\n", "test_out = test.eval(feed_dict={x:tbatch_xs})\n", - "print(\"time:\", (time.time()-start_time))\n", - "print(test_out, tcorrect.eval(feed_dict={y_:tbatch_ys}))" + "print(\"Time:\", (time.time()-start_time))\n", + "print(\"Test:\", test_out)\n", + "print(\"Real:\", tcorrect.eval(feed_dict={y_:tbatch_ys}))" ] }, { @@ -283,7 +299,9 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "sess.close()" + ] } ], "metadata": {