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修改了传入数组的类型为 floatX
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Lines changed: 42 additions & 33 deletions

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‎09. theano/09.02 theano basics.ipynb‎

Lines changed: 42 additions & 33 deletions
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@@ -31,9 +31,17 @@
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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"collapsed": false
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},
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"outputs": [],
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using gpu device 0: GeForce GTX 850M\n"
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]
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}
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],
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"source": [
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"import theano\n",
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"\n",
@@ -169,7 +177,7 @@
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"output_type": "stream",
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"text": [
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"<class 'theano.tensor.var.TensorVariable'>\n",
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"TensorType(float64, scalar)\n"
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"TensorType(float32, scalar)\n"
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]
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}
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],
@@ -202,7 +210,7 @@
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": true
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"collapsed": false
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},
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"outputs": [],
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"source": [
@@ -226,7 +234,7 @@
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{
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"data": {
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"text/plain": [
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"array(9.0)"
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"array(9.0, dtype=float32)"
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]
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},
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"execution_count": 9,
@@ -255,7 +263,7 @@
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{
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"data": {
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"text/plain": [
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"array(9.0)"
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"array(9.0, dtype=float32)"
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]
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},
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"execution_count": 10,
@@ -347,7 +355,7 @@
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"collapsed": true
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"collapsed": false
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},
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"outputs": [],
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"source": [
@@ -386,21 +394,23 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[array([ 18., 37.]), array(91.0)]\n"
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"[array([ 18., 37.], dtype=float32), array(91.0, dtype=float32)]\n"
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]
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}
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],
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"source": [
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"print linear_mix(np.array([[1, 2, 3],\n",
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" [4, 5, 6]]), #A\n",
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" np.array([1, 2, 3]), #x\n",
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" np.array([4, 5])) #b"
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" [4, 5, 6]], dtype=theano.config.floatX), #A\n",
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" np.array([1, 2, 3], dtype=theano.config.floatX), #x\n",
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" np.array([4, 5], dtype=theano.config.floatX)) #b"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"这里 `dtype=theano.config.floatX` 是为了与 `theano` 设置的浮点数精度保持一致，默认是 `float64`，但是在 `GPU` 上一般使用 `float32` 会更高效一些。\n",
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"\n",
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"我们还可以像定义普通函数一样，给 `theano` 函数提供默认值，需要使用 `theano.Param` 类："
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]
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},
@@ -412,7 +422,7 @@
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},
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"outputs": [],
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"source": [
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"linear_mix_default = theano.function([A, x, theano.Param(b, default=np.zeros(2))],\n",
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"linear_mix_default = theano.function([A, x, theano.Param(b, default=np.zeros(2, dtype=theano.config.floatX))],\n",
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" [y, z])"
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]
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},
@@ -434,14 +444,14 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[array([ 14., 32.]), array(91.0)]\n"
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"[array([ 14., 32.], dtype=float32), array(91.0, dtype=float32)]\n"
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]
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}
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],
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"source": [
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"print linear_mix_default(np.array([[1, 2, 3],\n",
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" [4, 5, 6]]), #A\n",
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" np.array([1, 2, 3])) #x"
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" [4, 5, 6]], dtype=theano.config.floatX), #A\n",
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" np.array([1, 2, 3], dtype=theano.config.floatX)) #x"
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]
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},
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{
@@ -462,15 +472,15 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[array([ 18., 37.]), array(91.0)]\n"
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"[array([ 18., 37.], dtype=float32), array(91.0, dtype=float32)]\n"
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]
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}
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],
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"source": [
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"print linear_mix_default(np.array([[1, 2, 3],\n",
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" [4, 5, 6]]), #A\n",
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" np.array([1, 2, 3]), #x\n",
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" np.array([4, 5])) #b"
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" [4, 5, 6]], dtype=theano.config.floatX), #A\n",
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" np.array([1, 2, 3], dtype=theano.config.floatX), #x\n",
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" np.array([4, 5], dtype=theano.config.floatX)) #b"
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]
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},
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{
@@ -498,12 +508,12 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"TensorType(float64, matrix)\n"
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"CudaNdarrayType(float32, matrix)\n"
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]
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}
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],
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"source": [
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"shared_var = theano.shared(np.array([[1.0, 2.0], [3.0, 4.0]]))\n",
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"shared_var = theano.shared(np.array([[1.0, 2.0], [3.0, 4.0]], dtype=theano.config.floatX))\n",
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"\n",
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"print shared_var.type"
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]
@@ -523,7 +533,7 @@
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},
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"outputs": [],
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"source": [
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"shared_var.set_value(np.array([[3.0, 4], [2, 1]]))"
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"shared_var.set_value(np.array([[3.0, 4], [2, 1]], dtype=theano.config.floatX))"
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]
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},
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{
@@ -544,7 +554,7 @@
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"data": {
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"text/plain": [
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"array([[ 3., 4.],\n",
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" [ 2., 1.]])"
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" [ 2., 1.]], dtype=float32)"
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]
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},
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"execution_count": 21,
@@ -613,7 +623,7 @@
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}
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],
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"source": [
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"shared_var.set_value(np.array([[1.0, 2], [3, 4]]))\n",
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"shared_var.set_value(np.array([[1.0, 2], [3, 4]], dtype=theano.config.floatX))\n",
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"\n",
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"print f()"
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]
@@ -629,7 +639,7 @@
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {
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"collapsed": true
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"collapsed": false
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},
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"outputs": [],
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"source": [
@@ -660,8 +670,7 @@
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"[[ 1. 2.]\n",
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" [ 3. 4.]]\n",
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"the return value:\n",
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"[[ 1. 2.]\n",
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" [ 3. 4.]]\n",
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"<CudaNdarray object at 0x000000003403E430>\n",
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"after update:\n",
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"[[ 0. 1.]\n",
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" [ 2. 3.]]\n"
@@ -673,7 +682,7 @@
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"print shared_var.get_value()\n",
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"\n",
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"print 'the return value:'\n",
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"print f_update(np.array([[1.0, 1], [1, 1]]))\n",
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"print f_update(np.array([[1.0, 1], [1, 1]], dtype=theano.config.floatX))\n",
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"\n",
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"print 'after update:'\n",
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"print shared_var.get_value()"
@@ -705,7 +714,7 @@
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{
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"data": {
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"text/plain": [
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"array(20.0)"
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"array(20.0, dtype=float32)"
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]
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},
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"execution_count": 26,
@@ -745,17 +754,17 @@
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Anaconda\\lib\\site-packages\\theano\\scan_module\\scan_perform_ext.py:133: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility\n",
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"C:\\Miniconda\\lib\\site-packages\\theano\\scan_module\\scan_perform_ext.py:133: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility\n",
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" from scan_perform.scan_perform import *\n"
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]
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}
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],
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"source": [
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"y_J = theano.gradient.jacobian(y, x)\n",
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"\n",
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"print y_J.eval({A: np.array([[9.0, 8, 7], [4, 5, 6]]), #A\n",
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" x: np.array([1.0, 2, 3]), #x\n",
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" b: np.array([4.0, 5])}) #b"
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"print y_J.eval({A: np.array([[9.0, 8, 7], [4, 5, 6]], dtype=theano.config.floatX), #A\n",
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" x: np.array([1.0, 2, 3], dtype=theano.config.floatX), #x\n",
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" b: np.array([4.0, 5], dtype=theano.config.floatX)}) #b"
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]
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},
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{

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