resolve merge conflict

Author: lukemarsden

dotscience/__init__.py

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+env

examples/.gitignore

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+{"0": "zero",
+ "1": "one",
+ "2": "two",
+ "3": "three",
+ "4": "four",
+ "5": "five",
+ "6": "six",
+ "7": "seven",
+ "8": "eight",
+ "9": "nine"}

examples/classes.json

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+export DOTSCIENCE_USERNAME=""
+export DOTSCIENCE_APIKEY=""
+export DOTSCIENCE_PROJECT_NAME=""
+export DOTSCIENCE_HOSTNAME=""

examples/env.sample

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+tensorflow==1.14.0
+#datadots-api==0.2.0

examples/requirements.txt

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+#!/bin/bash -e
+if [ ! -f env ]; then
+    echo "please copy env.sample to env and fill it in"
+    exit 1
+fi
+rm -rf model
+. ./env
+PYTHONPATH=..:$PYTHONPATH python3 train.py

examples/run.sh

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+from __future__ import print_function
+import dotscience as ds
+import os
+
+# defaults to connecting to prod
+ds.connect(
+    os.getenv("DOTSCIENCE_USERNAME"), 
+    os.getenv("DOTSCIENCE_APIKEY"), 
+    os.getenv("DOTSCIENCE_PROJECT_NAME"),
+    os.getenv("DOTSCIENCE_HOSTNAME")
+)
+
+import tensorflow as tf
+from tensorflow.keras.datasets import mnist
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, Flatten
+from tensorflow.keras.layers import Conv2D, MaxPooling2D
+from tensorflow.keras import backend as K
+
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+
+batch_size = ds.parameter("batch_size", 128)
+num_classes = ds.parameter("num_classes", 10)
+epochs = ds.parameter("epochs", 1)
+
+# input image dimensions
+img_rows, img_cols = 28, 28
+limit = ds.parameter("limit", 1000)
+
+# the data, split between train and test sets
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+x_train = x_train[:limit]
+y_train = y_train[:limit]
+
+if K.image_data_format() == 'channels_first':
+    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
+    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
+    input_shape = (1, img_rows, img_cols)
+else:
+    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
+    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
+    input_shape = (img_rows, img_cols, 1)
+
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255
+x_test /= 255
+print('x_train shape:', x_train.shape)
+print(x_train.shape[0], 'train samples')
+print(x_test.shape[0], 'test samples')
+
+# convert class vectors to binary class matrices
+y_train = tf.keras.utils.to_categorical(y_train, num_classes)
+y_test = tf.keras.utils.to_categorical(y_test, num_classes)
+
+model = Sequential()
+model.add(Conv2D(32, kernel_size=(3, 3),
+                 activation='relu',
+                 input_shape=input_shape))
+model.add(Conv2D(64, (3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+model.add(Flatten())
+model.add(Dense(128, activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(num_classes, activation='softmax'))
+
+model.compile(loss=tf.keras.losses.categorical_crossentropy,
+              optimizer=tf.keras.optimizers.Adam(),
+              metrics=['accuracy'])
+
+model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=epochs,
+          verbose=1,
+          validation_data=(x_test, y_test))
+score = model.evaluate(x_test, y_test, verbose=0)
+print('Test loss:', ds.metric("loss", score[0]))
+print('Test accuracy:', ds.metric("accuracy", score[1]))
+
+#model.save("model.h5")
+#tf.keras.experimental.export_saved_model(model, ds.model(tf, 'model'))
+
+
+tf.keras.experimental.export_saved_model(model, ds.output('model'))
+ds.model(tf, "mnist", "model", classes="classes.json")
+ds.publish("trained mnist model", deploy=True)