13 KiB
13 KiB
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## USE for Multi GPU Systems #import os #os.environ["CUDA_VISIBLE_DEVICES"]="0" import numpy as np import pandas as pd import tensorflow as tf # Importing SK-learn to calculate precision and recall import sklearn from sklearn import metrics target_names = ["Knuckle", "Finger"]
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# the data, split between train and test sets df = pd.read_pickle("DataStudyEvaluation/df_blobs_area.pkl")
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df.userID.unique()
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array([ 1, 3, 5, 6, 7, 8, 9, 10, 11, 12])
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x = np.vstack(df.Blobs) x = x.reshape(-1, 27, 15, 1)
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# convert class vectors to binary class matrices (one-hot notation) num_classes = 2 y = utils.to_categorical(df.InputMethod, num_classes)
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i = 1 plt.imshow(x[i].reshape(27, 15)) #np.sqrt(784) = 28 plt.title("Label for image %i is: %s" % (i, y[i]))
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Text(0.5, 1.0, 'Label for image 1 is: [1. 0.]')
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# If GPU is not available: # GPU_USE = '/cpu:0' #config = tf.ConfigProto(device_count = {"GPU": 1}) # If GPU is available: config = tf.ConfigProto() config.log_device_placement = True config.allow_soft_placement = True config.gpu_options.allow_growth=True config.gpu_options.allocator_type = 'BFC' # Limit the maximum memory used config.gpu_options.per_process_gpu_memory_fraction = 0.4 # set session config tf.keras.backend.set_session(tf.Session(config=config))
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loadpath = "./ModelSnapshots/CNN-33767.h5" model = tf.keras.models.load_model(loadpath)
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer. WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version. Instructions for updating: Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`. WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead.
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%%time lst = [] batch = 100 for i in range(0, len(x), batch): _x = x[i: i+batch] lst.extend(model.predict(_x))
CPU times: user 14.2 s, sys: 1.25 s, total: 15.5 s Wall time: 11 s
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df["InputMethodPred"] = lst df.InputMethodPred = df.InputMethodPred.apply(lambda x: np.argmax(x))
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df_eval = df
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print(sklearn.metrics.confusion_matrix(df_eval.InputMethod.values, df_eval.InputMethodPred.values, labels=[0, 1])) cm = sklearn.metrics.confusion_matrix(df_eval.InputMethod.values, df_eval.InputMethodPred.values, labels=[0, 1], ) cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] print(cm) print("Accuray: %.3f" % sklearn.metrics.accuracy_score(df_eval.InputMethod.values, df_eval.InputMethodPred.values)) print("Recall: %.3f" % metrics.recall_score(df_eval.InputMethod.values, df_eval.InputMethodPred.values, average="macro")) print("Precision: %.3f" % metrics.average_precision_score(df_eval.InputMethod.values, df_eval.InputMethodPred.values, average="macro")) print("F1-Score: %.3f" % metrics.f1_score(df_eval.InputMethod.values, df_eval.InputMethodPred.values, average="macro")) print(sklearn.metrics.classification_report(df_eval.InputMethod.values, df_eval.InputMethodPred.values, target_names=target_names))
[[21071 3651]
[ 2096 57783]]
[[0.85231777 0.14768223]
[0.03500392 0.96499608]]
Accuray: 0.932
Recall: 0.909
Precision: 0.932
F1-Score: 0.916
precision recall f1-score support
Knuckle 0.91 0.85 0.88 24722
Finger 0.94 0.96 0.95 59879
accuracy 0.93 84601
macro avg 0.93 0.91 0.92 84601
weighted avg 0.93 0.93 0.93 84601
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