diff --git a/translations/ar/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/ar/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index e1fdc598..2db06dfd 100644 --- a/translations/ar/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/ar/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/bg/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/bg/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 66187eab..67b6d052 100644 --- a/translations/bg/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/bg/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/bn/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/bn/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 454f52e9..737f6991 100644 --- a/translations/bn/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/bn/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -462,10 +462,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -477,7 +476,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/br/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/br/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 445fc88f..43ca7ac8 100644 --- a/translations/br/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/br/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/cs/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/cs/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 973c3222..634294f1 100644 --- a/translations/cs/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/cs/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/da/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/da/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 5feba90c..4d6c2183 100644 --- a/translations/da/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/da/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/de/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/de/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 3de9e2aa..8ac5ecaa 100644 --- a/translations/de/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/de/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/el/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/el/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 5e2b8ebb..7cc3fbbc 100644 --- a/translations/el/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/el/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/en/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/en/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 0f72715e..cf2c71d4 100644 --- a/translations/en/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/en/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/es/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/es/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 55c650fd..9e022b18 100644 --- a/translations/es/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/es/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/fa/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/fa/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 8401a344..b93a1a2f 100644 --- a/translations/fa/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/fa/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/fi/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/fi/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index a74c020d..a9f85e6e 100644 --- a/translations/fi/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/fi/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/fr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/fr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 1006e80d..2a0dce95 100644 --- a/translations/fr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/fr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/he/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/he/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 34682fd5..46d774c5 100644 --- a/translations/he/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/he/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -462,10 +462,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -477,7 +476,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/hi/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/hi/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index db26b8eb..4fe95a80 100644 --- a/translations/hi/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/hi/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/hk/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/hk/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 590429be..c841dbdb 100644 --- a/translations/hk/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/hk/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/hr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/hr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 17b1b399..19d99056 100644 --- a/translations/hr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/hr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/hu/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/hu/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index e51462c6..53e3949e 100644 --- a/translations/hu/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/hu/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/id/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/id/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index d6940e26..a33ae5c0 100644 --- a/translations/id/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/id/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/it/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/it/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index cf8c8521..325c7e2c 100644 --- a/translations/it/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/it/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/ja/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/ja/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index a90edc78..d3401903 100644 --- a/translations/ja/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/ja/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/ko/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/ko/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index b9d087d8..d1bb31ac 100644 --- a/translations/ko/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/ko/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/lt/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/lt/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 5eab6a5b..068598d8 100644 --- a/translations/lt/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/lt/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/mo/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/mo/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index dc15123c..0a32ad77 100644 --- a/translations/mo/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/mo/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/mr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/mr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 69e1d8bd..8052c793 100644 --- a/translations/mr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/mr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/ms/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/ms/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index e6d9e520..ef07fc9d 100644 --- a/translations/ms/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/ms/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/my/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/my/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index cf461bd3..8867c740 100644 --- a/translations/my/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/my/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -458,10 +458,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -473,7 +472,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/ne/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/ne/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index f8d7975f..1fb69431 100644 --- a/translations/ne/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/ne/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/nl/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/nl/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index e7f111ce..0b876219 100644 --- a/translations/nl/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/nl/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/no/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/no/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 326fcd86..af5bb572 100644 --- a/translations/no/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/no/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/pa/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/pa/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 43d633e2..cffeb7b5 100644 --- a/translations/pa/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/pa/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/pl/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/pl/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index fc264a9b..41ba6683 100644 --- a/translations/pl/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/pl/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/pt/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/pt/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 4b1e41a0..0d5610b6 100644 --- a/translations/pt/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/pt/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/ro/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/ro/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index bca5d4ee..c0269220 100644 --- a/translations/ro/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/ro/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/ru/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/ru/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 8847cab6..92fe62ca 100644 --- a/translations/ru/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/ru/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/sk/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/sk/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 859583ce..725caab2 100644 --- a/translations/sk/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/sk/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/sl/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/sl/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 763e4a55..b13e2505 100644 --- a/translations/sl/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/sl/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -462,10 +462,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -477,7 +476,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/sr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/sr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 15fa0e71..56354b93 100644 --- a/translations/sr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/sr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/sv/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/sv/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 4641c0a4..35f56cce 100644 --- a/translations/sv/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/sv/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/sw/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/sw/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index d1867381..61a01418 100644 --- a/translations/sw/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/sw/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/th/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/th/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 39266cda..e240ddc6 100644 --- a/translations/th/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/th/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/tl/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/tl/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 920ce58e..d51b5a38 100644 --- a/translations/tl/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/tl/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/tr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/tr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index e7dead5b..b44245cd 100644 --- a/translations/tr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/tr/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/tw/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/tw/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 80fd7271..8446f82b 100644 --- a/translations/tw/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/tw/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/uk/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/uk/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 666d8c23..70deda30 100644 --- a/translations/uk/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/uk/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/ur/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/ur/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 64ef6ea2..f898e783 100644 --- a/translations/ur/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/ur/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/vi/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/vi/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index 5d015369..9f561504 100644 --- a/translations/vi/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/vi/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ] diff --git a/translations/zh/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/zh/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb index c6911ac6..cf14c1fe 100644 --- a/translations/zh/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +++ b/translations/zh/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -460,10 +460,9 @@ " neg_out = np.dot(negative_examples, weights) \n", " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", - " # make correction a list so it is homogeneous to weights list then numpy array accepts\n", - " snapshots.append((np.concatenate(weights),[(pos_correct+neg_correct)/2.0,0,0]))\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", "\n", - " return np.array(snapshots)\n", + " return np.array(snapshots, dtype=object)\n", "\n", "snapshots = train_graph(pos_examples,neg_examples)\n", "\n", @@ -475,7 +474,7 @@ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", " pylab.ylabel('Accuracy')\n", " pylab.xlabel('Iteration')\n", - " pylab.plot(step, snapshots[step,1][0], \"bo\")\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", " pylab.show()\n", "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" ]