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fix bug
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perhapszzy authored and perhapszzy committed Jan 5, 2018
1 parent ac44fb4 commit 38fba9b
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61 changes: 35 additions & 26 deletions ...earning_with_TensorFlow/1.0.0/Chapter03/.ipynb_checkpoints/3. 完整神经网络样例程序-checkpoint.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
"collapsed": true
},
"outputs": [],
"source": [
Expand Down Expand Up @@ -51,7 +51,9 @@
"source": [
"a = tf.matmul(x, w1)\n",
"y = tf.matmul(a, w2)\n",
"cross_entropy = -tf.reduce_mean(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0))) \n",
"y = tf.sigmoid(y)\n",
"cross_entropy = -tf.reduce_mean(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0))\n",
" + (1 - y_) * tf.log(tf.clip_by_value(1 - y, 1e-10, 1.0)))\n",
"train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)"
]
},
Expand Down Expand Up @@ -85,33 +87,31 @@
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"w1: [[-0.81131822 1.48459876 0.06532937]\n",
" [-2.44270396 0.0992484 0.59122431]]\n",
"w2: [[-0.81131822]\n",
"[[-0.81131822 1.48459876 0.06532937]\n",
" [-2.4427042 0.0992484 0.59122431]]\n",
"[[-0.81131822]\n",
" [ 1.48459876]\n",
" [ 0.06532937]]\n",
"\n",
"\n",
"After 0 training step(s), cross entropy on all data is 0.0674925\n",
"After 1000 training step(s), cross entropy on all data is 0.0163385\n",
"After 2000 training step(s), cross entropy on all data is 0.00907547\n",
"After 3000 training step(s), cross entropy on all data is 0.00714436\n",
"After 4000 training step(s), cross entropy on all data is 0.00578471\n",
"After 0 training step(s), cross entropy on all data is 1.89805\n",
"After 1000 training step(s), cross entropy on all data is 0.655075\n",
"After 2000 training step(s), cross entropy on all data is 0.626172\n",
"After 3000 training step(s), cross entropy on all data is 0.615096\n",
"After 4000 training step(s), cross entropy on all data is 0.610309\n",
"\n",
"\n",
"w1: [[-1.9618274 2.58235407 1.68203783]\n",
" [-3.4681716 1.06982327 2.11788988]]\n",
"w2: [[-1.8247149 ]\n",
" [ 2.68546653]\n",
" [ 1.41819501]]\n"
"[[ 0.02476984 0.5694868 1.69219422]\n",
" [-2.19773483 -0.23668921 1.11438966]]\n",
"[[-0.45544702]\n",
" [ 0.49110931]\n",
" [-0.9811033 ]]\n"
]
}
],
Expand All @@ -121,25 +121,34 @@
" sess.run(init_op)\n",
" \n",
" # 输出目前（未经训练）的参数取值。\n",
" print \"w1:\", sess.run(w1)\n",
" print \"w2:\", sess.run(w2)\n",
" print \"\\n\"\n",
" print(sess.run(w1))\n",
" print(sess.run(w2))\n",
" print(\"\\n\")\n",
" \n",
" # 训练模型。\n",
" STEPS = 5000\n",
" for i in range(STEPS):\n",
" start = (i*batch_size) % 128\n",
" end = (i*batch_size) % 128 + batch_size\n",
" sess.run(train_step, feed_dict={x: X[start:end], y_: Y[start:end]})\n",
" sess.run([train_step, y, y_], feed_dict={x: X[start:end], y_: Y[start:end]})\n",
" if i % 1000 == 0:\n",
" total_cross_entropy = sess.run(cross_entropy, feed_dict={x: X, y_: Y})\n",
" print(\"After %d training step(s), cross entropy on all data is %g\" % (i, total_cross_entropy))\n",
" \n",
" # 输出训练后的参数取值。\n",
" print \"\\n\"\n",
" print \"w1:\", sess.run(w1)\n",
" print \"w2:\", sess.run(w2)"
" print(\"\\n\")\n",
" print(sess.run(w1))\n",
" print(sess.run(w2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
Expand All @@ -158,7 +167,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
"version": "2.7.13"
}
},
"nbformat": 4,
Expand Down
61 changes: 35 additions & 26 deletions Deep_Learning_with_TensorFlow/1.0.0/Chapter03/3. 完整神经网络样例程序.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
"collapsed": true
},
"outputs": [],
"source": [
Expand Down Expand Up @@ -51,7 +51,9 @@
"source": [
"a = tf.matmul(x, w1)\n",
"y = tf.matmul(a, w2)\n",
"cross_entropy = -tf.reduce_mean(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0))) \n",
"y = tf.sigmoid(y)\n",
"cross_entropy = -tf.reduce_mean(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0))\n",
" + (1 - y_) * tf.log(tf.clip_by_value(1 - y, 1e-10, 1.0)))\n",
"train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)"
]
},
Expand Down Expand Up @@ -85,33 +87,31 @@
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"w1: [[-0.81131822 1.48459876 0.06532937]\n",
" [-2.44270396 0.0992484 0.59122431]]\n",
"w2: [[-0.81131822]\n",
"[[-0.81131822 1.48459876 0.06532937]\n",
" [-2.4427042 0.0992484 0.59122431]]\n",
"[[-0.81131822]\n",
" [ 1.48459876]\n",
" [ 0.06532937]]\n",
"\n",
"\n",
"After 0 training step(s), cross entropy on all data is 0.0674925\n",
"After 1000 training step(s), cross entropy on all data is 0.0163385\n",
"After 2000 training step(s), cross entropy on all data is 0.00907547\n",
"After 3000 training step(s), cross entropy on all data is 0.00714436\n",
"After 4000 training step(s), cross entropy on all data is 0.00578471\n",
"After 0 training step(s), cross entropy on all data is 1.89805\n",
"After 1000 training step(s), cross entropy on all data is 0.655075\n",
"After 2000 training step(s), cross entropy on all data is 0.626172\n",
"After 3000 training step(s), cross entropy on all data is 0.615096\n",
"After 4000 training step(s), cross entropy on all data is 0.610309\n",
"\n",
"\n",
"w1: [[-1.9618274 2.58235407 1.68203783]\n",
" [-3.4681716 1.06982327 2.11788988]]\n",
"w2: [[-1.8247149 ]\n",
" [ 2.68546653]\n",
" [ 1.41819501]]\n"
"[[ 0.02476984 0.5694868 1.69219422]\n",
" [-2.19773483 -0.23668921 1.11438966]]\n",
"[[-0.45544702]\n",
" [ 0.49110931]\n",
" [-0.9811033 ]]\n"
]
}
],
Expand All @@ -121,25 +121,34 @@
" sess.run(init_op)\n",
" \n",
" # 输出目前（未经训练）的参数取值。\n",
" print \"w1:\", sess.run(w1)\n",
" print \"w2:\", sess.run(w2)\n",
" print \"\\n\"\n",
" print(sess.run(w1))\n",
" print(sess.run(w2))\n",
" print(\"\\n\")\n",
" \n",
" # 训练模型。\n",
" STEPS = 5000\n",
" for i in range(STEPS):\n",
" start = (i*batch_size) % 128\n",
" end = (i*batch_size) % 128 + batch_size\n",
" sess.run(train_step, feed_dict={x: X[start:end], y_: Y[start:end]})\n",
" sess.run([train_step, y, y_], feed_dict={x: X[start:end], y_: Y[start:end]})\n",
" if i % 1000 == 0:\n",
" total_cross_entropy = sess.run(cross_entropy, feed_dict={x: X, y_: Y})\n",
" print(\"After %d training step(s), cross entropy on all data is %g\" % (i, total_cross_entropy))\n",
" \n",
" # 输出训练后的参数取值。\n",
" print \"\\n\"\n",
" print \"w1:\", sess.run(w1)\n",
" print \"w2:\", sess.run(w2)"
" print(\"\\n\")\n",
" print(sess.run(w1))\n",
" print(sess.run(w2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
Expand All @@ -158,7 +167,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
"version": "2.7.13"
}
},
"nbformat": 4,
Expand Down
55 changes: 34 additions & 21 deletions ...earning_with_TensorFlow/1.4.0/Chapter03/.ipynb_checkpoints/3. 完整神经网络样例程序-checkpoint.ipynb
View file
Original file line number Diff line number Diff line change
Expand Up @@ -51,7 +51,9 @@
"source": [
"a = tf.matmul(x, w1)\n",
"y = tf.matmul(a, w2)\n",
"cross_entropy = -tf.reduce_mean(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0))) \n",
"y = tf.sigmoid(y)\n",
"cross_entropy = -tf.reduce_mean(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0))\n",
" + (1 - y_) * tf.log(tf.clip_by_value(1 - y, 1e-10, 1.0)))\n",
"train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)"
]
},
Expand Down Expand Up @@ -85,31 +87,33 @@
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"w1: [[-0.81131822 1.48459876 0.06532937]\n",
"[[-0.81131822 1.48459876 0.06532937]\n",
" [-2.4427042 0.0992484 0.59122431]]\n",
"w2: [[-0.81131822]\n",
"[[-0.81131822]\n",
" [ 1.48459876]\n",
" [ 0.06532937]]\n",
"\n",
"\n",
"After 0 training step(s), cross entropy on all data is 0.0674925\n",
"After 1000 training step(s), cross entropy on all data is 0.0163385\n",
"After 2000 training step(s), cross entropy on all data is 0.00907547\n",
"After 3000 training step(s), cross entropy on all data is 0.00714436\n",
"After 4000 training step(s), cross entropy on all data is 0.00578471\n",
"After 0 training step(s), cross entropy on all data is 1.89805\n",
"After 1000 training step(s), cross entropy on all data is 0.655075\n",
"After 2000 training step(s), cross entropy on all data is 0.626172\n",
"After 3000 training step(s), cross entropy on all data is 0.615096\n",
"After 4000 training step(s), cross entropy on all data is 0.610309\n",
"\n",
"\n",
"w1: [[-1.96182752 2.58235407 1.68203771]\n",
" [-3.46817183 1.06982315 2.11788988]]\n",
"w2: [[-1.82471502]\n",
" [ 2.68546653]\n",
" [ 1.41819501]]\n"
"[[ 0.02476984 0.5694868 1.69219422]\n",
" [-2.19773483 -0.23668921 1.11438966]]\n",
"[[-0.45544702]\n",
" [ 0.49110931]\n",
" [-0.9811033 ]]\n"
]
}
],
Expand All @@ -119,25 +123,34 @@
" sess.run(init_op)\n",
" \n",
" # 输出目前（未经训练）的参数取值。\n",
" print \"w1:\", sess.run(w1)\n",
" print \"w2:\", sess.run(w2)\n",
" print \"\\n\"\n",
" print(sess.run(w1))\n",
" print(sess.run(w2))\n",
" print(\"\\n\")\n",
" \n",
" # 训练模型。\n",
" STEPS = 5000\n",
" for i in range(STEPS):\n",
" start = (i*batch_size) % 128\n",
" end = (i*batch_size) % 128 + batch_size\n",
" sess.run(train_step, feed_dict={x: X[start:end], y_: Y[start:end]})\n",
" sess.run([train_step, y, y_], feed_dict={x: X[start:end], y_: Y[start:end]})\n",
" if i % 1000 == 0:\n",
" total_cross_entropy = sess.run(cross_entropy, feed_dict={x: X, y_: Y})\n",
" print(\"After %d training step(s), cross entropy on all data is %g\" % (i, total_cross_entropy))\n",
" \n",
" # 输出训练后的参数取值。\n",
" print \"\\n\"\n",
" print \"w1:\", sess.run(w1)\n",
" print \"w2:\", sess.run(w2)"
" print(\"\\n\")\n",
" print(sess.run(w1))\n",
" print(sess.run(w2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
Expand Down
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