From 7c5c8e303e51cd07cc147066dadfdb643adfaaac Mon Sep 17 00:00:00 2001 From: dvgodoy Date: Mon, 30 Apr 2018 23:48:15 +0200 Subject: [PATCH 1/4] including decision boundary function --- README.md | 44 +- deepreplay/plot.py | 2 +- deepreplay/replay.py | 98 + examples/circles_dataset.py | 17 +- examples/comparison_activation_functions.py | 56 + images/decision_boundary.png | Bin 0 -> 14505 bytes notebooks/circles_dataset.ipynb | 23794 ++++++------------ setup.py | 2 +- 8 files changed, 7361 insertions(+), 16652 deletions(-) create mode 100644 examples/comparison_activation_functions.py create mode 100644 images/decision_boundary.png diff --git a/README.md b/README.md index dbb4720..c9e0ad1 100644 --- a/README.md +++ b/README.md @@ -17,13 +17,14 @@ It contains: The available visualizations are: - ***Feature Space***: plot of a 2-D grid representing the twisted and turned feature space, corresponding to the output of a hidden layer (only 2-unit hidden layers supported for now); + - ***Decision Boundary***: plot of a 2-D grid representing the original feature space, together with the decision boundary (only 2-dimensional inputs supported for now); - ***Probabilities***: histograms of the resulting class probabilities for the inputs, corresponding to the output of the final layer (only binary classification supported for now); - ***Loss and Metric***: line plot for the loss and a chosen metric, computed over all the inputs; - ***Losses***: histogram of the losses computed over all the inputs (only binary cross-entropy loss suported for now). -Feature Space | Class Probability | Loss/Metric | Losses -:-:|:-:|:-:|:-: -![Feature Space](/images/feature_space.png) | ![Probability Histogram](/images/prob_histogram.png) | ![Loss and Metric](/images/loss_and_metric.png) | ![Loss Histogram](/images/loss_histogram.png) +Feature Space | Decision Boundary | Class Probability | Loss/Metric | Losses +:-:|:-:|:-:|:-:|:-: +![Feature Space](/images/feature_space.png) | ![Decision Boundary](/images/decision_boundary.png) | ![Probability Histogram](/images/prob_histogram.png) | ![Loss and Metric](/images/loss_and_metric.png) | ![Loss Histogram](/images/loss_histogram.png) ### Google Colab @@ -129,17 +130,43 @@ fs.animate().save('feature_space_animation.mp4', dpi=120, writer=writer) ## FAQ -### Grid lines are missing! +### 1. Grid lines are missing! Does your input have more than 2 dimensions? If so, this is expected, as grid lines are only plot for 2-dimensional inputs. If your input is 2-dimensional and grid lines are missing nonetheless, please open an [issue](https://github.com/dvgodoy/deepreplay/issues). -### My hidden layer has more than 2 units! How can I plot it anyway? +### 2. My hidden layer has more than 2 units! How can I plot it anyway? Apart from toy datasets, it is likely the (last) hidden layer has more than 2 units. But ***DeepReplay*** only supports ***FeatureSpace*** plots based on 2-unit hidden layers. So, what can you do? -Well, you can add an extra hidden layer with ***2 units*** and a ***LINEAR*** activation function and tell ****DeepReplay*** to use this layer for plotting the ***FeatureSpace***! +There are two different ways of handling this: if your inputs are 2-dimensional, you can plot them directly, together with the decision boundary. Otherwise, you can (train and) plot 2-dimensional embeddings. + +#### 2.1 Using Raw Inputs + +Instead of using ***FeatureSpace***, you can use ***DecisionBoundary*** and plot the inputs in their original feature space, with the decision boundary as of any given epoch. + +In this case, there is no need to specify any layer, as it will use the raw inputs. + +```python +## Input layer has 2 units +## Hidden layer has 10 units +model = Sequential() +model.add(Dense(input_dim=2, units=10, kernel_initializer='he', activation='tanh')) + +## Typical output layer for binary classification +model.add(Dense(units=1, kernel_initializer='normal', activation='sigmoid', name='output')) + +... + +fs = replay.build_decision_boundary(ax_fs) +``` + +For an example, check the [Circles Dataset](https://github.com/dvgodoy/deepreplay/blob/master/notebooks/circles_dataset.ipynb). + +#### 2.2 Using Embeddings + +You can add an extra hidden layer with ***2 units*** and a ***LINEAR*** activation function and tell ***DeepReplay*** to use this layer for plotting the ***FeatureSpace***! ```python ## Input layer has 57 units @@ -160,7 +187,10 @@ fs = replay.build_feature_space(ax_fs, layer_name='hidden') By doing so, you will be including a transformation from a highly dimensional space to a 2-dimensional space, which is also going to be learned by the network. -For examples, check either the [Circles Dataset](https://github.com/dvgodoy/deepreplay/blob/master/notebooks/circles_dataset.ipynb) or [UCI Spambase Dataset](https://github.com/dvgodoy/deepreplay/blob/master/notebooks/UCI_spambase_dataset.ipynb) notebooks. +In fact, you will be also training 2-dimensional embeddings, which will then feed the last layer. You can think of this as a logistic regression with 2 inputs, in this case, the embeddings. + +For examples, check either the [Moons Dataset](https://github.com/dvgodoy/deepreplay/blob/master/notebooks/moons_dataset.ipynb) or [UCI Spambase Dataset](https://github.com/dvgodoy/deepreplay/blob/master/notebooks/UCI_spambase_dataset.ipynb) notebooks. + ## Comments, questions, suggestions, bugs diff --git a/deepreplay/plot.py b/deepreplay/plot.py index 978f382..ba55b5e 100644 --- a/deepreplay/plot.py +++ b/deepreplay/plot.py @@ -6,7 +6,7 @@ import seaborn as sns from collections import namedtuple from matplotlib import animation -matplotlib.rcParams['animation.writer'] = 'ffmpeg' +matplotlib.rcParams['animation.writer'] = 'avconv' sns.set_style('white') FeatureSpaceData = namedtuple('FeatureSpaceData', ['line', 'bent_line', 'prediction', 'target']) diff --git a/deepreplay/replay.py b/deepreplay/replay.py index 7b80c9a..b4335e3 100644 --- a/deepreplay/replay.py +++ b/deepreplay/replay.py @@ -97,11 +97,13 @@ def __init__(self, replay_filename, group_name, model_filename=''): self._loss_hist_data = None self._loss_and_metric_data = None self._prob_hist_data = None + self._decision_boundary_data = None # Attributes for the visualizations - Plot objects self._feature_space_plot = None self._loss_hist_plot = None self._loss_and_metric_plot = None self._prob_hist_plot = None + self._decision_boundary_plot = None def _retrieve_weights(self): # Generates ranges for the number of different weight arrays in each layer @@ -124,6 +126,10 @@ def _make_function(self, inputs, layer): def _predict_proba(self, inputs, weights): return self._get_output([self.learning_phase, inputs] + weights) + @property + def decision_boundary(self): + return self._decision_boundary_plot, self._decision_boundary_data + @property def feature_space(self): return self._feature_space_plot, self._feature_space_data @@ -328,6 +334,98 @@ def build_probability_histogram(self, ax_negative, ax_positive, epoch_start=0, e self._prob_hist_plot = ProbabilityHistogram(ax_negative, ax_positive).load_data(self._prob_hist_data) return self._prob_hist_plot + def build_decision_boundary(self, ax, contour_points=1000, xlim=(-1, 1), ylim=(-1, 1), display_grid=True, + epoch_start=0, epoch_end=-1): + """Builds a FeatureSpace object to be used for plotting and + animating the raw inputs and the decision boundary. + The underlying data, that is, grid lines, inputs and contour + lines, as well as the corresponding predictions for the + contour lines, can be later accessed as the second element of + the `decision_boundary` property. + + Only inputs with 2 dimensions are supported! + + Parameters + ---------- + ax: AxesSubplot + Subplot of a Matplotlib figure. + contour_points: int, optional + Number of points in each axis of the contour. + Default is 1,000. + xlim: tuple of ints, optional + Boundaries for the X axis of the grid. + ylim: tuple of ints, optional + Boundaries for the Y axis of the grid. + display_grid: boolean, optional + If True, display grid lines (for 2-dimensional inputs). + Default is True. + epoch_start: int, optional + First epoch to consider. + epoch_end: int, optional + Last epoch to consider. + + Returns + ------- + decision_boundary_plot: FeatureSpace + An instance of a FeatureSpace object to make plots and + animations. + """ + input_dims = self.model.input_shape[-1] + assert input_dims == 2, 'Only layers with 2-dimensional inputs are supported!' + + if epoch_end == -1: + epoch_end = self.n_epochs + epoch_end = min(epoch_end, self.n_epochs) + + X = self.inputs + y = self.targets + + y_ind = y.squeeze().argsort() + X = X.squeeze()[y_ind].reshape(X.shape) + y = y.squeeze()[y_ind] + + n_classes = len(np.unique(y)) + + # Builds a 2D grid and the corresponding contour coordinates + grid_lines = np.array([]) + if display_grid: + grid_lines = build_2d_grid(xlim, ylim) + + contour_lines = build_2d_grid(xlim, ylim, contour_points, contour_points) + get_predictions = self._make_function(self.model.inputs, self.model.layers[-1]) + + bent_lines = [] + bent_inputs = [] + bent_contour_lines = [] + bent_preds = [] + # For each epoch, uses the corresponding weights + for epoch in range(epoch_start, epoch_end + 1): + weights = self.weights[epoch] + + bent_lines.append(grid_lines) + bent_inputs.append(X) + bent_contour_lines.append(contour_lines) + + inputs = [TEST_MODE, contour_lines.reshape(-1, 2)] + weights + output_shape = (contour_lines.shape[:2]) + (-1,) + # Makes predictions for each point in the contour surface + bent_preds.append((get_predictions(inputs=inputs)[0].reshape(output_shape) > .5).astype(np.int)) + + # Makes lists into ndarrays and wrap them as namedtuples + bent_inputs = np.array(bent_inputs) + bent_lines = np.array(bent_lines) + bent_contour_lines = np.array(bent_contour_lines) + bent_preds = np.array(bent_preds) + + line_data = FeatureSpaceLines(grid=grid_lines, input=X, contour=contour_lines) + bent_line_data = FeatureSpaceLines(grid=bent_lines, input=bent_inputs, contour=bent_contour_lines) + self._decision_boundary_data = FeatureSpaceData(line=line_data, bent_line=bent_line_data, + prediction=bent_preds, target=y) + + # Creates a FeatureSpace plot object and load data into it + self._decision_boundary_plot = FeatureSpace(ax, True).load_data(self._decision_boundary_data) + return self._decision_boundary_plot + def build_feature_space(self, ax, layer_name, contour_points=1000, xlim=(-1, 1), ylim=(-1, 1), scale_fixed=True, display_grid=True, epoch_start=0, epoch_end=-1): """Builds a FeatureSpace object to be used for plotting and diff --git a/examples/circles_dataset.py b/examples/circles_dataset.py index ac7efe6..7d5f8e0 100644 --- a/examples/circles_dataset.py +++ b/examples/circles_dataset.py @@ -15,7 +15,7 @@ X, y = make_circles(n_samples=2000, random_state=27, noise=0.03) -sgd = SGD(lr=0.01) +sgd = SGD(lr=0.02) he_initializer = he_normal(seed=42) normal_initializer = normal(seed=42) @@ -30,10 +30,6 @@ model.add(Dense(units=3, kernel_initializer=he_initializer)) model.add(Activation('relu')) -model.add(Dense(units=2, - kernel_initializer=normal_initializer, - activation='linear', - name='hidden')) model.add(Dense(units=1, kernel_initializer=normal_initializer, activation='sigmoid', @@ -43,7 +39,7 @@ optimizer=sgd, metrics=['acc']) -model.fit(X, y, epochs=300, batch_size=16, callbacks=[replaydata]) +model.fit(X, y, epochs=200, batch_size=16, callbacks=[replaydata]) replay = Replay(replay_filename='circles_dataset.h5', group_name=group_name) @@ -54,14 +50,13 @@ ax_lm = plt.subplot2grid((2, 4), (0, 3)) ax_lh = plt.subplot2grid((2, 4), (1, 3)) -fs = replay.build_feature_space(ax_fs, layer_name='hidden', - display_grid=False, scale_fixed=False) +fs = replay.build_decision_boundary(ax_fs, xlim=(-1.5, 1.5), ylim=(-1.5, 1.5)) ph = replay.build_probability_histogram(ax_ph_neg, ax_ph_pos) lh = replay.build_loss_histogram(ax_lh) lm = replay.build_loss_and_metric(ax_lm, 'acc') -sample_figure = compose_plots([fs, ph, lm, lh], 280) -sample_figure.savefig('circles.png', dpi=120, format='png') +sample_figure = compose_plots([fs, ph, lm, lh], 150) +sample_figure.savefig('circles_db.png', dpi=120, format='png') sample_anim = compose_animations([fs, ph, lm, lh]) -sample_anim.save(filename='circles.mp4', dpi=120, fps=5) +sample_anim.save(filename='circles_db.mp4', dpi=120, fps=5) diff --git a/examples/comparison_activation_functions.py b/examples/comparison_activation_functions.py new file mode 100644 index 0000000..0966f0f --- /dev/null +++ b/examples/comparison_activation_functions.py @@ -0,0 +1,56 @@ +from keras.layers import Dense +from keras.models import Sequential +from keras.optimizers import SGD +from keras.initializers import glorot_normal, normal + +from deepreplay.datasets.parabola import load_data +from deepreplay.callbacks import ReplayData +from deepreplay.replay import Replay +from deepreplay.plot import compose_animations, compose_plots + +import matplotlib.pyplot as plt + +X, y = load_data() + +sgd = SGD(lr=0.05) + +for activation in ['sigmoid', 'tanh', 'relu']: + glorot_initializer = glorot_normal(seed=42) + normal_initializer = normal(seed=42) + + replaydata = ReplayData(X, y, filename='comparison_activation_functions.h5', group_name=activation) + + model = Sequential() + model.add(Dense(input_dim=2, + units=2, + kernel_initializer=glorot_initializer, + activation=activation, + name='hidden')) + + model.add(Dense(units=1, + kernel_initializer=normal_initializer, + activation='sigmoid', + name='output')) + + model.compile(loss='binary_crossentropy', + optimizer=sgd, + metrics=['acc']) + + model.fit(X, y, epochs=150, batch_size=16, callbacks=[replaydata]) + +fig, axs = plt.subplots(1, 3, figsize=(12, 4)) + +replays = [] +for activation in ['sigmoid', 'tanh', 'relu']: + replays.append(Replay(replay_filename='comparison_activation_functions.h5', group_name=activation)) + +spaces = [] +for ax, replay in zip(axs, replays): + 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- "execution_count": 21, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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tW4u2eD1OnAA+/BBYuTJt539DCSUZc2LOaZvg0aOS4dGyJdUQl+HD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\n", 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SJckAcbkkrcxkkkwPr1e2r/DYY5IyB1y9KrSC0wn075/uU71padBAMlJMJi1D5VpERkrW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MN7px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\n3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"text/plain": [ "
" ] @@ -117,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -127,20 +135,18 @@ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", - "dense_4 (Dense) (None, 5) 15 \n", - "_________________________________________________________________\n", - "activation_3 (Activation) (None, 5) 0 \n", + "dense_1 (Dense) (None, 5) 15 \n", "_________________________________________________________________\n", - "dense_5 (Dense) (None, 3) 18 \n", + "activation_1 (Activation) (None, 5) 0 \n", "_________________________________________________________________\n", - "activation_4 (Activation) (None, 3) 0 \n", + "dense_2 (Dense) (None, 3) 18 \n", "_________________________________________________________________\n", - "hidden (Dense) (None, 2) 8 \n", + "activation_2 (Activation) (None, 3) 0 \n", "_________________________________________________________________\n", - "output (Dense) (None, 1) 3 \n", + "output (Dense) (None, 1) 4 \n", "=================================================================\n", - "Total params: 44\n", - "Trainable params: 44\n", + "Total params: 37\n", + "Trainable params: 37\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "None\n" @@ -166,14 +172,6 @@ " kernel_initializer=he_initializer))\n", "model.add(Activation('relu'))\n", "\n", - "# Added layer to allow plotting the feature space\n", - "# It has 2 units and uses a LINEAR activation, so the network will also learn the\n", - "# mapping from 3-dimensions to 2-dimensions\n", - "model.add(Dense(units=2,\n", - " kernel_initializer=normal_initializer,\n", - " activation='linear',\n", - " name='hidden'))\n", - "\n", "# Typical output layer for binary classification\n", "model.add(Dense(units=1,\n", " kernel_initializer=normal_initializer,\n", @@ -196,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": { "scrolled": true }, @@ -205,633 +203,433 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/300\n", - "2000/2000 [==============================] - 0s 46us/step - loss: 0.6932 - acc: 0.4865\n", - "Epoch 2/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6932 - acc: 0.4905\n", - "Epoch 3/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6932 - acc: 0.4865\n", - "Epoch 4/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6932 - acc: 0.4840\n", - "Epoch 5/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6933 - acc: 0.4895\n", - "Epoch 6/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6932 - acc: 0.4950\n", - "Epoch 7/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6932 - acc: 0.4775\n", - "Epoch 8/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6932 - acc: 0.4990\n", - "Epoch 9/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6932 - acc: 0.4885\n", - "Epoch 10/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6932 - acc: 0.4980\n", - "Epoch 11/300\n", - "2000/2000 [==============================] - 0s 41us/step - loss: 0.6932 - acc: 0.4900\n", - "Epoch 12/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6932 - acc: 0.4970\n", - "Epoch 13/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6932 - acc: 0.4815\n", - "Epoch 14/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6932 - acc: 0.5010\n", - "Epoch 15/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6932 - acc: 0.4870\n", - "Epoch 16/300\n", - "2000/2000 [==============================] - 0s 41us/step - loss: 0.6932 - acc: 0.4730\n", - "Epoch 17/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6932 - acc: 0.4920\n", - "Epoch 18/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6932 - acc: 0.4780\n", - "Epoch 19/300\n", - "2000/2000 [==============================] - 0s 40us/step - loss: 0.6932 - acc: 0.4845\n", - "Epoch 20/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6932 - acc: 0.4930\n", - "Epoch 21/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6932 - acc: 0.4960\n", - "Epoch 22/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6932 - acc: 0.4890\n", - "Epoch 23/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6932 - acc: 0.4950\n", - "Epoch 24/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6932 - acc: 0.4850\n", - "Epoch 25/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6932 - acc: 0.4890\n", - "Epoch 26/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6932 - acc: 0.4980\n", - "Epoch 27/300\n", - "2000/2000 [==============================] - 0s 40us/step - loss: 0.6932 - acc: 0.4905\n", - "Epoch 28/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6932 - acc: 0.4755\n", - "Epoch 29/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6932 - acc: 0.4960\n", - "Epoch 30/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6932 - acc: 0.4925\n", - "Epoch 31/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6932 - acc: 0.4975\n", - "Epoch 32/300\n", - "2000/2000 [==============================] - 0s 40us/step - loss: 0.6932 - acc: 0.4950\n", - "Epoch 33/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6932 - acc: 0.4975\n", - 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- "Epoch 125/300\n", - "2000/2000 [==============================] - 0s 40us/step - loss: 0.6915 - acc: 0.5335\n", - "Epoch 126/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6914 - acc: 0.5395\n", - "Epoch 127/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6913 - acc: 0.5455\n", - "Epoch 128/300\n", - "2000/2000 [==============================] - 0s 40us/step - loss: 0.6912 - acc: 0.5480\n", - "Epoch 129/300\n", - "2000/2000 [==============================] - 0s 37us/step - loss: 0.6911 - acc: 0.5385\n", - "Epoch 130/300\n", - "2000/2000 [==============================] - 0s 40us/step - loss: 0.6910 - acc: 0.5365\n", - "Epoch 131/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6909 - acc: 0.5400\n", - "Epoch 132/300\n", - "2000/2000 [==============================] - 0s 40us/step - loss: 0.6908 - acc: 0.5395\n", - "Epoch 133/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6906 - acc: 0.5445\n", - "Epoch 134/300\n", - "2000/2000 [==============================] - 0s 37us/step - loss: 0.6905 - acc: 0.5325\n", - "Epoch 135/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6904 - acc: 0.5400\n", - "Epoch 136/300\n", - "2000/2000 [==============================] - 0s 37us/step - loss: 0.6903 - acc: 0.5410\n", - "Epoch 137/300\n", - "2000/2000 [==============================] - 0s 37us/step - loss: 0.6901 - acc: 0.5310\n", - "Epoch 138/300\n", - "2000/2000 [==============================] - 0s 37us/step - loss: 0.6899 - acc: 0.5375\n", - "Epoch 139/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6898 - acc: 0.5410\n", - "Epoch 140/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6896 - acc: 0.5405\n", - "Epoch 141/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6894 - acc: 0.5335\n", - "Epoch 142/300\n", - "2000/2000 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- "Epoch 151/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6871 - acc: 0.5425\n", - "Epoch 152/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6867 - acc: 0.5365\n", - "Epoch 153/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6864 - acc: 0.5405\n", - "Epoch 154/300\n", - "2000/2000 [==============================] - 0s 40us/step - loss: 0.6861 - acc: 0.5330\n", - "Epoch 155/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6858 - acc: 0.5445\n", - "Epoch 156/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6854 - acc: 0.5385\n", - "Epoch 157/300\n", - "2000/2000 [==============================] - 0s 37us/step - loss: 0.6850 - acc: 0.5405\n", - "Epoch 158/300\n", - "2000/2000 [==============================] - 0s 37us/step - loss: 0.6846 - acc: 0.5405\n", - "Epoch 159/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6842 - acc: 0.5440\n", - "Epoch 160/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6838 - acc: 0.5410\n", - "Epoch 161/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6833 - acc: 0.5530\n", - "Epoch 162/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6829 - acc: 0.5430\n", - "Epoch 163/300\n", - "2000/2000 [==============================] - 0s 40us/step - loss: 0.6824 - acc: 0.5470\n", - "Epoch 164/300\n", - "2000/2000 [==============================] - 0s 43us/step - loss: 0.6819 - acc: 0.5395\n", - "Epoch 165/300\n", - "2000/2000 [==============================] - 0s 40us/step - loss: 0.6814 - acc: 0.5480\n", - "Epoch 166/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6809 - acc: 0.5440\n", - "Epoch 167/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6803 - acc: 0.5480\n", - "Epoch 168/300\n", - "2000/2000 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- "Epoch 177/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6739 - acc: 0.5505\n", - "Epoch 178/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6732 - acc: 0.5535\n", - "Epoch 179/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6723 - acc: 0.5550\n", - "Epoch 180/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6715 - acc: 0.5530\n", - "Epoch 181/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6706 - acc: 0.5555\n", - "Epoch 182/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6695 - acc: 0.5605\n", - "Epoch 183/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6687 - acc: 0.5565\n", - "Epoch 184/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6675 - acc: 0.5615\n", - "Epoch 185/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6664 - acc: 0.5655\n", - "Epoch 186/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6651 - acc: 0.5705\n", - "Epoch 187/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6638 - acc: 0.5730\n", - "Epoch 188/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6625 - acc: 0.5815\n", - "Epoch 189/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6609 - acc: 0.5760\n", - "Epoch 190/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6591 - acc: 0.5855\n", - "Epoch 191/300\n", - "2000/2000 [==============================] - 0s 41us/step - loss: 0.6571 - acc: 0.5840\n", - "Epoch 192/300\n", - "2000/2000 [==============================] - 0s 40us/step - loss: 0.6552 - acc: 0.5905\n", - "Epoch 193/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6529 - acc: 0.6030\n", - "Epoch 194/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6506 - acc: 0.6125\n", - "Epoch 195/300\n", - "2000/2000 [==============================] - 0s 37us/step - loss: 0.6480 - acc: 0.6210\n", - "Epoch 196/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6448 - acc: 0.6160\n", - "Epoch 197/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6416 - acc: 0.6440\n", - "Epoch 198/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6380 - acc: 0.6445\n", - "Epoch 199/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6340 - acc: 0.6405\n", - "Epoch 200/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6293 - acc: 0.6640\n", - "Epoch 201/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6243 - acc: 0.6685\n", - "Epoch 202/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6187 - acc: 0.6895\n", - "Epoch 203/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.6129 - acc: 0.6945\n", - "Epoch 204/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.6062 - acc: 0.7065\n", - "Epoch 205/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.5994 - acc: 0.7110\n", - "Epoch 206/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.5924 - acc: 0.7075\n", - "Epoch 207/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.5848 - acc: 0.7190\n", - "Epoch 208/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.5770 - acc: 0.7155\n", - "Epoch 209/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.5690 - acc: 0.7105\n", - "Epoch 210/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.5594 - acc: 0.7185\n", - "Epoch 211/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.5515 - acc: 0.7180\n", - "Epoch 212/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.5407 - acc: 0.7205\n", - "Epoch 213/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.5288 - acc: 0.7345\n", - "Epoch 214/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.5162 - acc: 0.7450\n", - "Epoch 215/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.5019 - acc: 0.7635\n", - "Epoch 216/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.4859 - acc: 0.7735\n", - "Epoch 217/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.4681 - acc: 0.8025\n", - "Epoch 218/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.4472 - acc: 0.8215\n", - "Epoch 219/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.4167 - acc: 0.8705\n", - "Epoch 220/300\n", - "2000/2000 [==============================] - 0s 37us/step - loss: 0.3851 - acc: 0.9125\n", - "Epoch 221/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.3539 - acc: 0.9375\n", - "Epoch 222/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.3226 - acc: 0.9590\n", - "Epoch 223/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.2927 - acc: 0.9635\n", - "Epoch 224/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.2658 - acc: 0.9660\n", - "Epoch 225/300\n", - "2000/2000 [==============================] - 0s 37us/step - loss: 0.2392 - acc: 0.9680\n", - "Epoch 226/300\n", - "2000/2000 [==============================] - 0s 46us/step - loss: 0.2151 - acc: 0.9785\n", - "Epoch 227/300\n", - "2000/2000 [==============================] - 0s 48us/step - loss: 0.1935 - acc: 0.9790\n", - "Epoch 228/300\n", - "2000/2000 [==============================] - 0s 49us/step - loss: 0.1733 - acc: 0.9820\n", - "Epoch 229/300\n", - "2000/2000 [==============================] - 0s 40us/step - loss: 0.1583 - acc: 0.9795\n", - "Epoch 230/300\n", - "2000/2000 [==============================] - 0s 41us/step - loss: 0.1438 - acc: 0.9840\n", - "Epoch 231/300\n", - "2000/2000 [==============================] - 0s 45us/step - loss: 0.1315 - acc: 0.9835\n", - "Epoch 232/300\n", - "2000/2000 [==============================] - 0s 43us/step - loss: 0.1230 - acc: 0.9805\n", - "Epoch 233/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.1125 - acc: 0.9870\n", - "Epoch 234/300\n", - "2000/2000 [==============================] - 0s 40us/step - loss: 0.1053 - acc: 0.9835\n", - "Epoch 235/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.0992 - acc: 0.9845\n", - "Epoch 236/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.0914 - acc: 0.9870\n", - "Epoch 237/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.0873 - acc: 0.9850\n", - "Epoch 238/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.0831 - acc: 0.9840\n", - "Epoch 239/300\n", - "2000/2000 [==============================] - 0s 40us/step - loss: 0.0773 - acc: 0.9865\n", - "Epoch 240/300\n", - "2000/2000 [==============================] - 0s 40us/step - loss: 0.0750 - acc: 0.9855\n", - "Epoch 241/300\n", - "2000/2000 [==============================] - 0s 40us/step - loss: 0.0690 - acc: 0.9890\n", - "Epoch 242/300\n", - "2000/2000 [==============================] - 0s 40us/step - loss: 0.0687 - acc: 0.9870\n", - "Epoch 243/300\n", - "2000/2000 [==============================] - 0s 39us/step - loss: 0.0650 - acc: 0.9875\n", - "Epoch 244/300\n", - "2000/2000 [==============================] - 0s 38us/step - loss: 0.0628 - acc: 0.9875\n", - "Epoch 245/300\n" + "2000/2000 [==============================] - 0s 85us/step - loss: 0.6218 - acc: 0.6775\n", + "Epoch 84/200\n", + "2000/2000 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"Epoch 93/200\n", + "2000/2000 [==============================] - 0s 85us/step - loss: 0.5903 - acc: 0.7055\n", + "Epoch 94/200\n", + "2000/2000 [==============================] - 0s 86us/step - loss: 0.5867 - acc: 0.7125\n", + "Epoch 95/200\n", + "2000/2000 [==============================] - 0s 90us/step - loss: 0.5831 - acc: 0.7010\n", + "Epoch 96/200\n", + "2000/2000 [==============================] - 0s 89us/step - loss: 0.5798 - acc: 0.7145\n", + "Epoch 97/200\n", + "2000/2000 [==============================] - 0s 90us/step - loss: 0.5757 - acc: 0.7145\n", + "Epoch 98/200\n", + "2000/2000 [==============================] - 0s 89us/step - loss: 0.5721 - acc: 0.7150\n", + "Epoch 99/200\n", + "2000/2000 [==============================] - 0s 86us/step - loss: 0.5680 - acc: 0.7175\n", + "Epoch 100/200\n", + "2000/2000 [==============================] - 0s 85us/step - loss: 0.5641 - acc: 0.7180\n", + "Epoch 101/200\n", + "2000/2000 [==============================] - 0s 90us/step - loss: 0.5600 - acc: 0.7295\n", + "Epoch 102/200\n", + "2000/2000 [==============================] - 0s 86us/step - loss: 0.5559 - acc: 0.7205\n", + "Epoch 103/200\n", + "2000/2000 [==============================] - 0s 85us/step - loss: 0.5514 - acc: 0.7370\n", + "Epoch 104/200\n", + "2000/2000 [==============================] - 0s 88us/step - loss: 0.5475 - acc: 0.7265\n", + "Epoch 105/200\n", + "2000/2000 [==============================] - 0s 86us/step - loss: 0.5431 - acc: 0.7410\n", + "Epoch 106/200\n", + "2000/2000 [==============================] - 0s 92us/step - loss: 0.5389 - acc: 0.7450\n", + "Epoch 107/200\n", + "2000/2000 [==============================] - 0s 84us/step - loss: 0.5342 - acc: 0.7485\n", + "Epoch 108/200\n", + "2000/2000 [==============================] - 0s 88us/step - loss: 0.5293 - acc: 0.7595\n", + "Epoch 109/200\n", + "2000/2000 [==============================] - 0s 85us/step - loss: 0.5249 - acc: 0.7715\n", + "Epoch 110/200\n", + "2000/2000 [==============================] - 0s 90us/step - loss: 0.5198 - acc: 0.7750\n", + "Epoch 111/200\n", + "2000/2000 [==============================] - 0s 88us/step - loss: 0.5147 - acc: 0.7915\n", + "Epoch 112/200\n", + "2000/2000 [==============================] - 0s 84us/step - loss: 0.5096 - acc: 0.8045\n", + "Epoch 113/200\n", + "2000/2000 [==============================] - 0s 84us/step - loss: 0.5044 - acc: 0.8020\n", + "Epoch 114/200\n", + "2000/2000 [==============================] - 0s 89us/step - loss: 0.4992 - acc: 0.8195\n", + "Epoch 115/200\n", + "2000/2000 [==============================] - 0s 90us/step - loss: 0.4929 - acc: 0.8215\n", + "Epoch 116/200\n", + "2000/2000 [==============================] - 0s 83us/step - loss: 0.4861 - acc: 0.8330\n", + "Epoch 117/200\n", + "2000/2000 [==============================] - 0s 85us/step - loss: 0.4737 - acc: 0.8490\n", + "Epoch 118/200\n", + "2000/2000 [==============================] - 0s 91us/step - loss: 0.4610 - acc: 0.8750\n", 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90us/step - loss: 0.3628 - acc: 0.9795\n", + "Epoch 128/200\n", + "2000/2000 [==============================] - 0s 86us/step - loss: 0.3528 - acc: 0.9775\n", + "Epoch 129/200\n", + "2000/2000 [==============================] - 0s 85us/step - loss: 0.3424 - acc: 0.9855\n", + "Epoch 130/200\n", + "2000/2000 [==============================] - 0s 83us/step - loss: 0.3327 - acc: 0.9900\n", + "Epoch 131/200\n", + "2000/2000 [==============================] - 0s 90us/step - loss: 0.3226 - acc: 0.9865\n", + "Epoch 132/200\n", + "2000/2000 [==============================] - 0s 86us/step - loss: 0.3126 - acc: 0.9865\n", + "Epoch 133/200\n", + "2000/2000 [==============================] - 0s 87us/step - loss: 0.3030 - acc: 0.9860\n", + "Epoch 134/200\n", + "2000/2000 [==============================] - 0s 87us/step - loss: 0.2941 - acc: 0.9900\n", + "Epoch 135/200\n", + "2000/2000 [==============================] - 0s 91us/step - loss: 0.2848 - acc: 0.9915\n", + "Epoch 136/200\n", + "2000/2000 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+ "Epoch 192/200\n", + "2000/2000 [==============================] - 0s 84us/step - loss: 0.0640 - acc: 0.9970\n", + "Epoch 193/200\n", + "2000/2000 [==============================] - 0s 86us/step - loss: 0.0629 - acc: 0.9965\n", + "Epoch 194/200\n", + "2000/2000 [==============================] - 0s 91us/step - loss: 0.0622 - acc: 0.9970\n", + "Epoch 195/200\n", + "2000/2000 [==============================] - 0s 85us/step - loss: 0.0611 - acc: 0.9965\n", + "Epoch 196/200\n", + "2000/2000 [==============================] - 0s 88us/step - loss: 0.0600 - acc: 0.9970\n", + "Epoch 197/200\n", + "2000/2000 [==============================] - 0s 87us/step - loss: 0.0593 - acc: 0.9965\n", + "Epoch 198/200\n", + "2000/2000 [==============================] - 0s 85us/step - loss: 0.0583 - acc: 0.9965\n", + "Epoch 199/200\n", + "2000/2000 [==============================] - 0s 88us/step - loss: 0.0572 - acc: 0.9970\n", + "Epoch 200/200\n", + "2000/2000 [==============================] - 0s 89us/step - loss: 0.0567 - acc: 0.9970\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model.fit(X, y, epochs=300, batch_size=16, callbacks=[replaydata])" + "model.fit(X, y, epochs=200, batch_size=16, callbacks=[replaydata])" ] }, { @@ -843,12 +641,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -875,7 +673,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": { "collapsed": true }, @@ -883,8 +681,7 @@ "source": [ "replay = Replay(replay_filename='circles_dataset.h5', group_name=group_name)\n", "\n", - "fs = replay.build_feature_space(ax_fs, layer_name='hidden',\n", - " display_grid=False, scale_fixed=False)\n", + "fs = replay.build_decision_boundary(ax_fs, xlim=(-1.5, 1.5), ylim=(-1.5, 1.5))\n", "ph = replay.build_probability_histogram(ax_ph_neg, ax_ph_pos)\n", "lh = replay.build_loss_histogram(ax_lh)\n", "lm = replay.build_loss_and_metric(ax_lm, 'acc')" @@ -899,23 +696,23 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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U8b0QdACRxEuCtRrgz3ePpHathkHHInJAMr68mPZ3Dgg6DJFSMWnpJKZ+s2dvzOQtKTQY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\nJVgiIiIiIiI5ogRLREREREQkR/5/7fsEHl754yoAAAAASUVORK5CYII=\n", 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86NED+patzmrMUUNEMoD9quoXkUzgROA9VS1KcGjGGFNty5cv58QTT+Scc87B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\n/HooZWUaIWI7CoYt6OhwmtO6sSKmv5I7IhYKQdN+Z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" ] }, - "execution_count": 11, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "sample_figure = compose_plots([fs, ph, lm, lh], 280)\n", + "sample_figure = compose_plots([fs, ph, lm, lh], 150)\n", "sample_figure" ] }, @@ -928,16003 +725,6736 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" @@ -16933,7 +7463,7 @@ "" ] }, - "execution_count": 12, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } diff --git a/setup.py b/setup.py index 69e4daf..f092ccd 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ def readme(): return f.read() setup(name='deepreplay', - version='0.1.0a5', + version='0.1.0a6', install_requires=['matplotlib', 'numpy', 'h5py', 'seaborn', 'keras', 'sklearn'], description='"Hyper-parameters in Action!" visualizing tool for Keras models.', long_description=readme(), From 1c2d47f68ced06f81838262412b379f4413a7c80 Mon Sep 17 00:00:00 2001 From: dvgodoy Date: Tue, 1 May 2018 22:58:34 +0200 Subject: [PATCH 2/4] added custom title --- README.md | 8 +++---- deepreplay/plot.py | 24 +++++++++++++++++++-- examples/comparison_activation_functions.py | 10 +++++---- 3 files changed, 32 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index c9e0ad1..0644441 100644 --- a/README.md +++ b/README.md @@ -16,9 +16,9 @@ It contains: - a class ***Replay***, which leverages the collected data to build several kinds of visualizations. The available visualizations are: - - ***Feature Space***: plot of a 2-D grid representing the twisted and turned feature space, corresponding to the output of a hidden layer (only 2-unit hidden layers supported for now); + - ***Feature Space***: plot representing the twisted and turned feature space, corresponding to the output of a hidden layer (only 2-unit hidden layers supported for now), including grid lines if the input is 2-dimensional; - ***Decision Boundary***: plot of a 2-D grid representing the original feature space, together with the decision boundary (only 2-dimensional inputs supported for now); - - ***Probabilities***: histograms of the resulting class probabilities for the inputs, corresponding to the output of the final layer (only binary classification supported for now); + - ***Probabilities***: two histograms of the resulting classification probabilities for the inputs, corresponding to the output of the final layer (only binary classification supported for now); - ***Loss and Metric***: line plot for the loss and a chosen metric, computed over all the inputs; - ***Losses***: histogram of the losses computed over all the inputs (only binary cross-entropy loss suported for now). @@ -164,7 +164,7 @@ fs = replay.build_decision_boundary(ax_fs) For an example, check the [Circles Dataset](https://github.com/dvgodoy/deepreplay/blob/master/notebooks/circles_dataset.ipynb). -#### 2.2 Using Embeddings +#### 2.2 Using a Latent Space You can add an extra hidden layer with ***2 units*** and a ***LINEAR*** activation function and tell ***DeepReplay*** to use this layer for plotting the ***FeatureSpace***! @@ -187,7 +187,7 @@ fs = replay.build_feature_space(ax_fs, layer_name='hidden') By doing so, you will be including a transformation from a highly dimensional space to a 2-dimensional space, which is also going to be learned by the network. -In fact, you will be also training 2-dimensional embeddings, which will then feed the last layer. You can think of this as a logistic regression with 2 inputs, in this case, the embeddings. +In fact, you will be learning 2-dimensional latent space, which will then feed the last layer. You can think of this as a logistic regression with 2 inputs, in this case, the latent factors. For examples, check either the [Moons Dataset](https://github.com/dvgodoy/deepreplay/blob/master/notebooks/moons_dataset.ipynb) or [UCI Spambase Dataset](https://github.com/dvgodoy/deepreplay/blob/master/notebooks/UCI_spambase_dataset.ipynb) notebooks. diff --git a/deepreplay/plot.py b/deepreplay/plot.py index ba55b5e..db6a535 100644 --- a/deepreplay/plot.py +++ b/deepreplay/plot.py @@ -150,7 +150,7 @@ def compose_plots(objects, epoch, title=''): title += ' - ' fig.suptitle('{}Epoch {}'.format(title, epoch), fontsize=14) fig.tight_layout() - fig.subplots_adjust(top=0.9) + fig.subplots_adjust(top=0.85) return fig class Basic(object): @@ -158,6 +158,7 @@ class Basic(object): """ def __init__(self, ax): self._title = '' + self._custom_title = '' self.n_epochs = 0 self.ax = ax @@ -166,7 +167,11 @@ def __init__(self, ax): @property def title(self): - return self._title if isinstance(self._title, tuple) else (self._title,) + title = self._title + if not isinstance(title, tuple): + title = (self._title,) + title = tuple([' '.join([self._custom_title, t]) for t in title]) + return title @property def axes(self): @@ -183,6 +188,20 @@ def _prepare_plot(self): def _update(i, object, epoch_start=0): pass + def set_title(self, title): + """Prepends a custom title to the plot. + + Parameters + ---------- + title: String + Custom title to prepend. + + Returns + ------- + None + """ + self._custom_title = title + def plot(self, epoch): """Plots data at a given epoch. @@ -353,6 +372,7 @@ class ProbabilityHistogram(Basic): """ def __init__(self, ax1, ax2): self._title = ('Negative Cases', 'Positive Cases') + self._custom_title = '' self.ax1 = ax1 self.ax2 = ax2 self.ax1.clear() diff --git a/examples/comparison_activation_functions.py b/examples/comparison_activation_functions.py index 0966f0f..1035ae1 100644 --- a/examples/comparison_activation_functions.py +++ b/examples/comparison_activation_functions.py @@ -45,12 +45,14 @@ replays.append(Replay(replay_filename='comparison_activation_functions.h5', group_name=activation)) spaces = [] -for ax, replay in zip(axs, replays): - spaces.append(replay.build_feature_space(ax, layer_name='hidden')) +for ax, replay, activation in zip(axs, replays, ['sigmoid', 'tanh', 'relu']): + space = replay.build_feature_space(ax, layer_name='hidden') + space.set_title(activation) + spaces.append(space) sample_figure = compose_plots(spaces, 80) sample_figure.savefig('comparison.png', dpi=120, format='png') -sample_anim = compose_animations(spaces) -sample_anim.save(filename='comparison.mp4', dpi=120, fps=5) +#sample_anim = compose_animations(spaces) +#sample_anim.save(filename='comparison.mp4', dpi=120, fps=5) From 6f5b1dbecee1a6934419b5a271aec25b1c90b1c3 Mon Sep 17 00:00:00 2001 From: dvgodoy Date: Wed, 2 May 2018 23:31:23 +0200 Subject: [PATCH 3/4] small fixes --- README.md | 4 ++-- setup.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 0644441..71a94b5 100644 --- a/README.md +++ b/README.md @@ -140,7 +140,7 @@ If your input is 2-dimensional and grid lines are missing nonetheless, please op Apart from toy datasets, it is likely the (last) hidden layer has more than 2 units. But ***DeepReplay*** only supports ***FeatureSpace*** plots based on 2-unit hidden layers. So, what can you do? -There are two different ways of handling this: if your inputs are 2-dimensional, you can plot them directly, together with the decision boundary. Otherwise, you can (train and) plot 2-dimensional embeddings. +There are two different ways of handling this: if your inputs are 2-dimensional, you can plot them directly, together with the decision boundary. Otherwise, you can (train and) plot 2-dimensional latent space. #### 2.1 Using Raw Inputs @@ -187,7 +187,7 @@ fs = replay.build_feature_space(ax_fs, layer_name='hidden') By doing so, you will be including a transformation from a highly dimensional space to a 2-dimensional space, which is also going to be learned by the network. -In fact, you will be learning 2-dimensional latent space, which will then feed the last layer. You can think of this as a logistic regression with 2 inputs, in this case, the latent factors. +In fact, the model will be learning a 2-dimensional latent space, which will then feed the last layer. You can think of this as a logistic regression with 2 inputs, in this case, the latent factors. For examples, check either the [Moons Dataset](https://github.com/dvgodoy/deepreplay/blob/master/notebooks/moons_dataset.ipynb) or [UCI Spambase Dataset](https://github.com/dvgodoy/deepreplay/blob/master/notebooks/UCI_spambase_dataset.ipynb) notebooks. diff --git a/setup.py b/setup.py index f092ccd..2dacc30 100644 --- a/setup.py +++ b/setup.py @@ -6,7 +6,7 @@ def readme(): setup(name='deepreplay', version='0.1.0a6', - install_requires=['matplotlib', 'numpy', 'h5py', 'seaborn', 'keras', 'sklearn'], + install_requires=['matplotlib', 'numpy', 'h5py', 'seaborn', 'keras', 'scikit-learn'], description='"Hyper-parameters in Action!" visualizing tool for Keras models.', long_description=readme(), long_description_content_type='text/markdown', From 1f9f937667714b9e999835d3bca0785b25682479 Mon Sep 17 00:00:00 2001 From: dvgodoy Date: Wed, 2 May 2018 23:42:00 +0200 Subject: [PATCH 4/4] small fixes --- deepreplay/plot.py | 2 +- examples/circles_dataset.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/deepreplay/plot.py b/deepreplay/plot.py index db6a535..a6af21e 100644 --- a/deepreplay/plot.py +++ b/deepreplay/plot.py @@ -6,7 +6,7 @@ import seaborn as sns from collections import namedtuple from matplotlib import animation -matplotlib.rcParams['animation.writer'] = 'avconv' +matplotlib.rcParams['animation.writer'] = 'ffmpeg' sns.set_style('white') FeatureSpaceData = namedtuple('FeatureSpaceData', ['line', 'bent_line', 'prediction', 'target']) diff --git a/examples/circles_dataset.py b/examples/circles_dataset.py index 7d5f8e0..3f59304 100644 --- a/examples/circles_dataset.py +++ b/examples/circles_dataset.py @@ -56,7 +56,7 @@ lm = replay.build_loss_and_metric(ax_lm, 'acc') sample_figure = compose_plots([fs, ph, lm, lh], 150) -sample_figure.savefig('circles_db.png', dpi=120, format='png') +sample_figure.savefig('circles.png', dpi=120, format='png') sample_anim = compose_animations([fs, ph, lm, lh]) -sample_anim.save(filename='circles_db.mp4', dpi=120, fps=5) +sample_anim.save(filename='circles.mp4', dpi=120, fps=5)